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Akkermansia muciniphila
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BACTERIA TO TREAT TYPE 2 DIABETES | Akkermansia Muciniphila

by Shamsul

BACTERIA TO TREAT TYPE 2 DIABETES | Akkermansia Muciniphila

Article Summary :


Obesity, flabbiness and type 2 diabetes are associated with low-grade inflammation. Among the new mechanisms, the link with intestinal bacteria seems more and more convincing. This intestinal microbiota would play a key role in triggering inflammation and insulin resistance via different mechanisms, such as the translocation of bacteria or even bacterial compounds with the development of metabolic endotoxemia. Certain intestinal bacteria could also contribute in a deleterious or, on the contrary, beneficial way to the improvement of carbohydrate homeostasis. Among the potential candidates, the role of Akkermansia muciniphila is currently being investigated.


Introduction


Obesity and overweight are classically associated with low-grade inflammation. This inflammation contributes to the development of insulin resistance, type 2 diabetes, and other cardio-metabolic complications. Over the past decade, many studies have associated the intestinal microbiota (formerly called: intestinal flora) with the development of these metabolic disorders (1,2). Over the past twenty years, our laboratory has contributed to a better understanding and elucidation of how the intestinal microbiota manages to dialogue with our body and contributes to the development of obesity and its associated metabolic disorders (insulin resistance, type 2, metabolic inflammation, non-alcoholic fatty liver disease (NASH)) (3-5)

 
Metabolic Endotoxemia
 

Among the candidates involved in this inflammation, we have proposed that constituents of the wall of intestinal bacteria (gram-negative), such as lipopolysaccharides (LPS) (also called endotoxins), would play an essential role in the triggering of some of these disorders (6 ). LPS are powerful pro-inflammatory molecules, continuously produced by the intestinal microbiota and whose absorption is directly linked to the ingestion of dietary lipids (3,7). Indeed, intestinal bacteria would contribute to the inflammation associated with metabolic disorders by mechanisms involving, in particular, an increase in plasma LPS levels, which we have defined as “metabolic endotoxemia” (3). 
 
   
 
The existence of metabolic endotoxemia and its role in triggering inflammation and insulin resistance associated with obesity and type-2 diabetes was first demonstrated experimentally in animals. Still, it was later largely confirmed in humans (3.7-9). From an experimental point of view, we demonstrated that a chronic infusion of LPS mimicking metabolic endotoxemia induces inflammation and insulin resistance associated with hepatic steatosis.
 
In addition, the invalidation of the LPS receptor (CD14/Toll-like receptor 4 (TLR-4)) protects against the development of various metabolic disorders induced by LPS or a high-fat diet.
 
 
Dialogues Between Bacteria and Host Cells
 

More recently, we demonstrated that the established dialogue between gut bacteria and host gut cells plays a prime role in the development of obesity and diabetes. By specifically inactivating (in gut epithelial cells) an innate immune system protein called MyD881 can reduce inflammation, insulin resistance, and type-2 diabetes associated with a high-fat diet ( 10). This protection is directly dependent on the composition and activity of intestinal bacteria, thus suggesting that intestinal cells play a key role in the systemic response to components of the intestinal microbiota.
 
In this context, the barrier function of the intestine is essential in order to limit as much as possible the passage of undesirable compounds from the intestinal lumen to the blood circulation and the tissues of the host. The effectiveness of this intestinal barrier is ensured by different cell types and different mechanisms (tight junction proteins, mucus layer, antimicrobial proteins and immunoglobulins, etc.) (10).
 
Among the mechanisms potentially involved in the bacteria-host dialogue, we found that the gut microbiota interacts closely with the endocannabinoid (eCB) system and its bioactive lipids (11,12). Indeed, the eCB system is involved in the control of the barrier function of the intestine, and certain bacteria (or intestinal microbiota) would be associated either with protection or, on the contrary, with the triggering of disorders of the intestinal barrier for review (6).
 
 
More recently, we discovered that the eCB system in adipose tissue, specifically the enzyme for the synthesis of N-acyl ethanolamines (NAPE-PLD), plays a key role in the regulation of energy metabolism (11). This substance (enzyme) is involved in the synthesis of bioactive molecules, some of which are already known for their effects on inflammation and appetite regulation.
 
Using genetic tools, we discovered that eliminating the enzyme specifically in adipocytes leads to obesity and insulin resistance. This is associated with an almost complete disappearance of beige cells, thus indicating an inability to oxidize fat. The absence of NAPE-PLD in this organ also prevents the development of beige cells during exposure to cold, preventing the mice from expending energy to produce heat.
 
Our work shows that animals without NAPE-PLD in adipose tissue develop inflammation associated with metabolic endotoxemia. In agreement with this observation, the composition of the bacteria in the intestine of these animals is also different.
   
 
This surprising result, therefore, suggests that adipose tissue interacts with the intestine and bacteria. Our work suggests that certain bioactive lipids may modify metabolism through a microbiota-host metabolic dialogue. But this dialogue does not only take place in the direction of adipose tissue to the intestine. Indeed, transferring gut bacteria from these mice into axenic animals causes a decrease in browning/beiging and fat oxidation, thus suggesting that gut bacteria would be able to control the metabolism of adipose tissue.
 
 
MyD88, or Myeloid Differentiation Primary Response Gene 88, is Involved in the Signaling Pathway of Most TLRs.
 
 
A place of choice for certain candidates?
 
Akkermansia Muciniphila
 

Various studies have shown an association between intestinal microbiota composition, body weight, hyperglycemia, and type-2 diabetes. Many metagenomic analyzes show that certain bacteria or bacterial families. They increase or decrease in the feces of type-2 diabetic patients compared to non-diabetic individuals. Still, there is no reliable “listing” to suggest a causal link between these observations and the onset of diabetes.
 
On the other hand, there is another bacterium called Akkermansia muciniphila which is interesting in the context of metabolic disorders. Indeed, this bacterium has been associated with energy and carbohydrate metabolism on several occasions. For example, we found this bacterium was less present in obese and type 2 diabetic mice, regardless of the model, i.e., genetic or nutritional (13,14). Next, we demonstrated that the administration of Akkermansia muciniphila to obese and diabetic animals reduced body weight gain, improved blood sugar and insulin resistance, strengthened the intestinal barrier, and decreased metabolic inflammation (14); other teams have recently confirmed these observations (15,16).
 
In humans, various studies have shown that the presence of Akkermansia muciniphila was inversely correlated with body weight or blood sugar (17-19). Note, however, that the abundance of this bacterium is increased by taking metformin, which makes it a confounding factor during its investigation in the intestine of type-2 diabetic patients (20,21).
 
Akkermansia Muciniphila
 
Our recent work has shown that the abundance of Akkermansia muciniphila could predict a patient’s response and metabolic improvements following a low-calorie diet. Specifically, subjects with more Akkermansia muciniphila before the nutritional intervention will show the greatest improvement in insulin sensitivity, greater decrease in total and LDL cholesterol, and waist circumference. Akkermansia muciniphila was also inversely correlated with fasting blood glucose but also with other parameters, such as the hip-to-waist ratio and the size of adipocytes in subcutaneous adipose tissue (17).
 
To date, no intervention study has been able to demonstrate whether this bacterium has any potential health benefits. This hypothesis is currently being investigated at the Cliniques Universitaires Saint-Luc in collaboration with Professors Jean-Paul Thissen, Michel Hermans, Dominique Maiter and Doctor Audrey Loumaye.
   
 
In conclusion, a lot of work is underway, and over a relatively short period of a decade, a significant number of advances have been made. Obviously, the influence of the intestinal microbiota on carbohydrate and energy homeostasis is complex and multifactorial. However, some leads could be suggested for the specific management of metabolic syndrome. Current research encourages the development of new therapeutic targets that will be personalized. Both targets, such as immunity or even different bacterial metabolites, are of particular interest.
 
 
Keywords
 
Gut microbiota, diabetes, inflammation, Akkermansia muciniphila, Health
 
 
 

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Références
 
     
  1. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, et al. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA 2004; 101, 15718-15723.
     
    2.  
  2. Cani PD, Delzenne NM. The role of the gut microbiota in energy metabolism and metabolic disease. Curr Pharm Des 2009; 15, 1546-1558.
     
    3.  
  3. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 2007; 56: 1761-1772.
     
    4.  
  4. Cani PD, Delzenne NM. Gut microflora as a target for energy and metabolic homeostasis. Curr Opin Clin Nutr Metab Care 2007;10: 729-734.
     
    5.  
  5. Cani PD, Everard A. Talking microbes: When gut bacteria interact with diet and host organs. Mol Nutr Food Res 2016; 60 (1):58-66.
     
    6.  
  6. Cani PD, Plovier H, Van Hul MV, Geurts L, Delzenne NM, Druart C, Everard A. Endocannabinoids [mdash] at the crossroads between the gut microbiota and host metabolism. Nature Rev Endocrinol 2016 Mar;12(3):133-43.
     
    7.  
  7. Amar J, Burcelin R, Ruidavets JB, Cani PD, Fauvel J, Alessi MC, et al. Energy intake is associated with endotoxemia in apparently healthy men. Am J Clin Nutr 2008; 87: 1219-1223.
     
    8.  
  8. Lassenius MI, Pietilainen KH, Kaartinen, K, Pussinen PJ, Syrjanen J, Forsblom C, et al. Bacterial endotoxin activity in human serum is associated with dyslipidemia, insulin resistance, obesity, and chronic inflammation. Diabetes Care 2011; 34: 1809-1815.
     
    9.  
  9. Pussinen PJ, Havulinna AS, Lehto M, Sundvall J, Salomaa V. Endotoxemia is associated with an increased risk of incident diabetes. Diabetes Care 2011; 34: 392-397.
     
    10.  
  10. Everard A, Geurts L, Caesar R, Van Hul M, Matamoros S, Duparc T, et al. Intestinal epithelial MyD88 is a sensor switching host metabolism towards obesity according to nutritional status. Nature Communications 2014; 5: 5648.
     
    11.  
  11. Geurts L, Everard A, Van Hul M, Essaghir A, Duparc T, Matamoros S, et al. Adipose tissue NAPE-PLD controls fat mass development by altering the browning process and gut microbiota. Nature Communications 2015; 6: 6495.
     
    12.  
  12. Muccioli GG, Naslain D, Backhed F, Reigstad CS, Lambert DM, Delzenne NM, Cani PD. The endocannabinoid system links gut microbiota to adipogenesis. Mol Syst Biol 2010; 6: 392.
     
    13.  
  13. Everard A, Lazarevic V, Derrien M, Girard M, Muccioli GG, Neyrinck AM, et al. Responses of gut microbiota and glucose and lipid metabolism to prebiotics in genetic obese and diet-induced leptin-resistant mice. Diabetes 2011; 60: 2775-2786.
     
    14.  
  14. Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB, et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc Natl Acad Sci USA 2013; 110: 9066-9071.
     
    15.  
  15. Org E, Parks BW, Joo JW, Emert B, Schwartzman W, Kang EY, et al. Genetic and environmental control of host-gut microbiota interactions. Genome Res 2015; 25(10):1558-69.
     
    16.  
  16. Shin NR, Lee JC, Lee HY, Kim MS, Whon TW, Lee MS, Bae JW. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 2014; 63: 727-735.
     
    17.  
  17. Dao MC, Everard A, Aron-Wisnewsky J, Sokolovska N, Prifti E, Verger EO, et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 2016 Mar;65(3):426-36.
     
    18.  
  18. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013; 500: 541-546.
     
    19.  
  19. Zhang X, Shen D, Fang Z, Jie Z, Qiu X, Zhang C, et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One 2013; 8: e71108.
     
    20.  
  20. Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier, E, Sunagawa S, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 2015; 528 : 262-266.
     
    21.  
  21. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490: 55-60. 22.
     
    22.  
  22. Plovier H, Everard A, Druart C, Depommier C, Van Hul M, Geurts, et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nature Medecine 2017; 23:107-113.
     
    23.  
 
 
 
 
 
 
 
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  Personally Controlled Electronic Health Record  
 
 
HealthWriting
 
How PCEHR Implementation Influences Countries’ Medical Systems
 
 by Shamsul   
 
 
 
 
 
 
How PCEHR Implementation Influences Countries’ Medical Systems
  
The National E-Health Transition Authority (NEHTA) has given a new definition upon previously called HER into PCEHR. It stands out from A Personally Controlled Electronic Health Record, which has the same function as EHR. PCHER is an electronic record of one’s medical history, which is recorded and shared in a secure network system. This secured information is only accessible to the authorized medical providers and the individual, in which the medical providers can deliver correct decisions related to the individual’s treatment and required procedures related to their health condition. But, the individual can still give additional information on the PCEHR based on their current health.
 
Remember that EHR and PCEHR are two different matters though using both terms is mostly transposable. Go on reading this report for a better understanding of the difference between the two terms.
 
 
How EHR and PCEHR Relate to Each Other 
 
EHR and PCEHR share similar characteristics, though each is categorized as a different system of records. Yet, both of them still relate to each other.
 
One major drawback of EHR is its inability to involve important information about a user or a patient. Definitely, it is the opposite with the Commonwealth Department of Health and Aged Care, outlining that the patient or users should be the most important individual in all kinds of health care systems. A patient should be able to ask for help and further medical advice from specialists, physicians, or other medical experts after treatment and diagnosis. EHR collects all data and information from various sources though it cannot get the exact data from the user or the patient. 
 
The previous aims of EHR were to increase and promote patient quality, though it should have addressed the importance of patient input. EHR was only accessible to the medical providers instead of the patients or users though they should have contributed their health condition information to their medical providers. Some agree with this concept because it relates to the patient’s privacy though it is against the basic outline of the patient’s rights.
 
 
Such a concept still applies to most medical record systems in third-world countries. But developed countries, including Australia, have started to use the PCEHR concept. This is because PCEHR has greater advantages, as compared to EHR. PCEHR allows patients and their medical advisers to have open communication so that patients can better understand their medical records and their self-management related to them. Patients can edit or add some important information related to their health condition.
   
 
So, PCEHR is the advanced form of EHR in which its design is notably the revision of a much better system than EHR. PCEHR’s main focus is the user’s or patients’ importance. On the other hand, EHR only emphasizes on medical hub center of the medical hub. However, it was considered an excellent medical record so that all patients did not worry too much about their conditions and could keep their records secure. Both PCEHR and EHR actually share the same function though each of them focuses on different subjects. The basic relation between the two is that the systems which are applied on PCEHR will not substitute other systems on medical systems records. Most government institutions from developed countries prefer to extend the use of PCEHR through the old method; EHR remains the only medical record system that medical advisers prefer.
 
 
Basic Approach of PCEHR System
 
Some countries have considered PCEHR as the best medical record system as they have successfully implemented it, with the endorsement of the federal government or some health organization. PCEHR was based on the ‘Concept of Operations’ draft, launched in June 2012 by NEHTA.
 
Australia is one of several highly developed countries which applied PCEHR after the $466.7 million investment from The Australian Federal Government in 2010. The system registration and participation currently have been used under very strong control by the federal government. The application of the system has enabled all citizens of Australia to view important information about their health condition in a secure view.
 
The system allows all Australians to share their information with reliable medical organizations to access the information online. It can deliver easier healthcare decisions by authorized medical professionals so that patients can get better treatments for their health conditions.
 
 
Most Australians believe that PCEHR will be implemented in the next few years. Yet, some still launch criticism of the implementation of the National PCEHR. It is due to the ‘rush out’ application which may not work well and meet any expectation that NEHTA determines. The President of the Medical Software Industry Association, Jon Hughes, criticized the program’s launching as inappropriate when it relates to its functionalities. Besides, the Australian Medical Association has delivered a statement that underlined that the applied PCEHR could be a bogus concurrence when it fails to combine the online record system with medical advisers’ software.
 
Yet, there is another important matter related to the PCEHR implementation. The Senate Community Affairs Committee, the Council of Australian Governments, the Federal Health Department, and Aus Tender have found out that the records analysis reached $760 million from its initial investment, which was $466.7 million from the Federal Government. The significant increase—which rea
 
Ches for about three hundred million dollars—has launched several opinions, which state that the national PCEHR implementation may be ineffective. This is because NEHTA received the entire initial investment on the first day of the PCEHR implementation.
   
 
 
Indivo – Norway
 
There is a short description of Indivo. Before continuing this essay, it should be noted that Indivo is unrelated to a Norwegian Health Institution or any other organization from the country. It is different from the proposed system name, which has been developed by the Norwegian Institutions, too. It is presently a multi-party project among three big medical institutions. They are Children’s Hospital Boston, MIT, and Harvard School of Medicine. Indigo has a system similar to PCEHR for complementing other applications from third parties and enhancing their functions and performance.
 
A slight difference between Indivo and PCEHR is that everyone can contribute to an individual’s medical records. Either they are individuals, governmental authorities, or any organization. They can even change the system when necessary, though there should be some agreements that state that there will be no incentive in the form of materialistic and commercial will exchange for the activities.
 
The Norwegian has maneuvered some plans continually for the standardization of guidelines and practices for clinical-related matters strategically since 2007. It is the main reason for both Social Care (KITHH) and Norwegian Health Informatics Authorities (NHIA) development. KITHH allowed the outline for implementing national standardization. Yet, in turn, it also allowed the integration possibility of the Indivo PCEHR system. Through both frameworks, there will be a percentage of Norway’s PCEHR system once it matches the current Hodemelding method. It becomes ongoing Norway’s Health sector of communication infrastructure.
 
So far, there is no schedule for the HER implementation for Norway’s Health Care Reform. Yet, it is evident through some documents that there will be some determined strategies for enabling the technology in the future. The project of Indivo remains in the speculative phase. It means some matters related to the use or implementation and use are still unpredictable.
 
 
NPÖ – Sweden
 
Sweden is another developed country that has implemented the outlines of the EHR system since 2010. It is called National Patient Overview (NPO). Based on Barcelona’s World of Health IT 2010, the Swedish Federal Government and affiliated health organizations have been developing the system slowly. Yet, the system has been fully implemented at the end of 2012. NPO provides some tools related to some points below:
 
     
  • Summaries of patient’s health condition
    •  
  • Complete information about the diagnosis
    •  
  • History of medication progress
    •  
  • Care services
    •  
 
“The system is already improving patient safety, shortening lead times and preventing the unnecessary transport,” stated Britt Marie, a person who deals with NPO operations in Örebro. Fortunately, this system implementation has been great though there were some issues related to the development of the program.
   
 
“Policy makers and implementers spent a year establishing the legal context, patient consent and the IT infrastructure for NPS. A company called Tieto was selected as the prime contractor, implementing and hosting the service using InterSystems Healthcare information exchange platform,” stated FutureGov.com.
 
All in all, the project is now at the stage of successfully implemented, as compared to other developed countries.
 
 
Future Implementation Schemes of PCEHR and HER
 
Let’s discuss the future implementation scheme of PCEHR and EHR from a more advanced point of view. The full implementation of PCEHR and EHR systems does not meet most people’s expectations, though there are still some exceptions. Several unidentified aspects led to some improperly addressed considerations about both systems’ implementation.
 
 
The Enhanced Use and Purpose of PCEHR and EHR
 
So far, the rate of EHR implementation in hospitals is slow. However, this system is effective enough in helping all medical practices as well as other kinds of additional analysis related to diagnosis and different types of medical treatments. It is shown that only ten percent of the hospital in the United States applied EHR systems in 2009, and less than two percent of them used the system comprehensively. It is due to the EHR’s system roll-out, which did not meet the basic standards of medical purposes. The essential standardization strictly obstructed EHR implementation, only providing a declaration of basic EHR stage functionality. All governmental authorities and other health organizations must ensure that the desired functionality can be implemented well so the system can work properly.
 
 
Presenting Interoperability
 
Other important aspects of enabling the EHR system are integrating and operating it seamlessly. The ongoing design of EHR design is managed at shallow application because there need to be more guidelines for formalized systems. It leads to the absence of proper EHR applications in which the hospital network infrastructure must be more supportive. People may not expect too much about existing interoperability in the future.
 
 
Formal Standard
 
In general, there were so many reasons why some mismatched standards have become the major obstacle in the implementation of both EHR and PCEHR. The author has concluded that when the health factor in any country needs to have a suitable transition method into an EHR system, they should pay attention to the compatibility issue, which relates a lot to the national stage. The related government and health organizations should have proper procedures and guidelines for implementing an EHR.
 
 
 
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  Surrogate Mother  
 
 
Writing
 
Surrogacy Case for A New Medical Student
 
 by Shamsul   
 
 
 
 
 
 
Surrogacy Case for A New Medical Student
 
 
As a medical student of the First Year, a surprising question was asked. 
 
 
 
 
Reflect on a time when you questioned or challenged a belief or idea. 
 
 
Describe what prompted your thinking and what was the outcome? 
 
If applicable, expand on how this experience will affect your practice as a future physician.
 
 
 
 
 
Case Study
 
This was when we came across a case during my first year as a medical student, which was that of a surrogacy case of a patient in the first trimester of her pregnancy. The case was rare in our professors’ obstetric practice. After the involvement in an extensive counseling session, Tracy finally agreed on a surrogate mother for a gay couple. Upon her regular visits, she told all about the surrogacy contract, which included that she would be entitled to compensation for all expenses incurred. And that she will report all prenatal visits, refrain from risky behaviors like drinking alcohol, smoking, and keep an open eye over medical records so that the couple has complete information about the ongoing pregnancy.
 
One day, during her visit, after regular examination, she was somewhat hesitant in saying something. Upon further inquiry, Tracy said that she thought there would be no trouble in not drinking alcohol when she signed the surrogacy contract, but now she is finding it difficult to give up. After doing some research, she further said that she concluded that alcohol consumption would not harm the baby, and she has started taking a glass or two of wine in a week. She also requested not to put anything about this in the medical record. 
 
Being a medical student, medical ethics holds great importance for us. Patient autonomy is certainly a greater concern where, in this case, Tracy has lost it by entering into the contract. Patients and surrogates are supposed to make their healthcare decisions after informed consent. This will involve discussing risks, options, and benefits to the patient and the decision maker’s invoking elements of their relatively stable value system to choose amongst the available options. 
   
 
Surrogacy has Raised Several Debates: 
 
Surrogacy has raised several debates in the past. The primary concern raised in the entire system relates to the concern about commodification, exploitation, and/or coercion when women got the money to become pregnant and deliver babies. However, according to my opinion and beliefs, the counter of this can be the right of the woman to enter into a contract; she can make decisions regarding her body. Womb commodification is that process in which the services performed by the female womb are for sale or purchase in the market. 
 
Basically, the market transaction of the female womb renders it to be only a service provider in the market. This arrangement argues whether women have control over their bodies or are exploited for individual body parts. Additionally, usually womb commodification most of the time takes advantage of the willingness of the poor to undertake a task as long as they are earning a wage.
 
In the current scenario, Tracy should have refrained from such risky behavior. Her willingness to discuss considered an opportunity to appreciate her responsible behavior. Which will further strengthen the relationship to facilitate communication in the future.
 
A Rare Case in Medical Records: 
 
Being a rare case in my medical record, I have taken this case over to my attending physicians who have claimed this case to be both an ethical and clinical dilemma that is unique to surrogacy pregnancy. He further advised that steps need that helps in placing the health of the women and the child first irrespective of the surrogacy contract. This can be eventually done by the provision of appropriate information about dose-related impacts of alcohol along with encouraging Tracy to avoid unforeseeable events in the future. The advice was to conduct counseling sessions for Tracy so that she clearly understands the impact of alcohol on her health status and that she comes to fulfill the terms of the surrogacy contract. 
   
 
An honest dialogue with Tracy that medical ethics does not permit to hide medical information; however, an evidence-based discussion with that of the intended parents so as to provide them with a complete picture of the scenario, make proper arrangements for appropriate follow-ups along with offering Tracy additional resources for proper counseling in this aspect can serve as better options. Additionally, I have a strong opinion that a thorough understanding of psychology, medicine, and law related to this particularly important clinical activity is an absolute obligation towards making surrogacy successful.
 
Surrogacy Contract:
 
Handling this case, I have realized that surrogacy does undermine the control of women over their bodies and places them in the position of emotional mercy of the intended parents. However, making the health of the surrogate to be the top priority offers them to be more empowered and given great responsibility. This makes the situation quite complex and involves making decisions that are in the favor of both the surrogate mother and the intended parents. Because Tracy has presented her free consent while entering into a surrogacy contract, she becomes ethically and morally liable to fulfill the contract terms and refrain from any such activity that causes harm to her health or to the fetus during the course of pregnancy. 
 
This eventually calls for the statement that although autonomy is an integral element of a surrogate mother; however, there are still some exceptions that directly relate to the health of the surrogate woman or the fetus in her womb.
   
 
 
“A new baby is like the beginning of all things-wonder, hope, a dream of possibilities.”
 
 
 
 
 
 
 
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HealthManagementScholarly
 
Edge AI (Artificial Intelligence) – IoT in Healthcare – New Opportunities, Case Studies
 
 by Shamsul   
 
 
 
 
 
 
 
Internet of Things in Healthcare
 
New Opportunities, Challenges, Case Studies, and Applications of Edge AI for Connected Healthcare
 
Introduction 
 
Healthcare is a very crucial aspect of human life. Because of the rapid growth in population and the rise of various diseases, there is also a drastic surge in the demand for healthcare and related facilities. In addition to this, the evolution of technology and the emergence of the Internet of Things (IoT), AI combined with advancing next-generation wireless communications, the notion of connected or smart healthcare has now been introduced to be a developed version of the traditional systems of healthcare. As a matter of fact, smart healthcare can be rendered as that health service that utilizes advanced technologies such as the Internet of Things (IoT), wearable devices along with advanced communication protocols so as to connect patients, the caregivers as well as healthcare institutions in a dynamic way so that information can be transformed easily among them [1].
 
Healthcare services which are categorized as smart healthcare services have made the possibility of handling as well as responding to different types of medical requests distantly in an intellectual manner hence, reducing the factor of hospitalization to a greater extent along with helping healthcare providers and other people to detect, predict, diagnose as well as treat the diseases in an intelligent way. In addition to this, smart healthcare e-services are also very useful in the prevention and controlling of the outbreak of infectious or contagious diseases like avian influenza, Ebola virus, Chickungunya virus [2], and the recent Covid-19 pandemic. With all these pieces of information in hand, it has become empirical that the adoption of smart healthcare services is the best way to improve the health level of any country.
 
An increasing amount of interest is being developed towards those architectures which tend to realize the cooperation of Fog, Cloud, and Edge computing in the healthcare sector. The primary aim is to exploit the potential of edge nodes so as to not only handle functional tasks but, also in data processing, analysis, inference, and correlation [3].
 
These approaches tend to make the future of the implementation of reliable distributed healthcare services and applications promising as the intelligent mapping of resource management and computational tasks across the nodes proves to fulfill the stringent requirements of the Internet of Things in the healthcare sector [4].
 
Within this context, the spread of ‘Edge/Fog Health’ solutions which uses appropriate computing models so as to distribute health sensors data processing as well as storage amongst various nodes that are situated at different levels of user proximity, as follows:
 
Edge computing tends to incur directly over devices to which the sensors are connected or any gateway device which is physically proximate to the sensors. Some good examples of edge nodes include wearable devices like smartphones.
 
Fog computing nodes are the local area network so that bigger and powerful devices like gateways, local servers, and PCs can be included which might be at a distance physically from the actuators and sensors.
 
Both of these paradigms which are increasingly being implemented in a combined manner tend to leverage the proximity of the users so as to deliver location-aware healthcare services with lower latency and higher availability [5]. Within these dimensions, some methods rely on hierarchical computing strategies that were proposed to allocate as well as distribute the inference tasks of Artificial Intelligence (AI) and Machine Learning (ML) methods between the fog, cloud, and edge levels attempting to push computational capacities of the edge devices to their top [1], [4], [6] and [7].
 
A transition from the mobile cloud computing model (MCC) which is characterized by increased data transmission costs as well as limited coverage towards a mobile edge computing model (MEC) [8] with lower latency and reliable edge ML approaches has been progressively been made part within the smart healthcare realm.
 
Edge Technology in Smart Healthcare
 
In recent times, most healthcare systems have implemented cloud computing solutions for the provision of accessible and affordable healthcare solutions so as to process as well as store large amounts of data that have been recorded through various biosensors. However, as cloud-based smart healthcare system usually involves a network, cloud servers, and mobile devices, there are many times a long distance amongst systems’ unit which, further strengthens the issue of higher latency within these systems [9]. Hence, real-time and emergency medical treatments cannot completely rely on cloud-based healthcare systems. In addition to this, the huge amount of information that is produced by these sensors should regularly be transferred over to the cloud so as to process and store information which can result in increased consumption of energy and increased cost as well. Moreover, many patients requiring treatment for chronic diseases require lower-cost mobile environments which lack support from cloud-based healthcare solutions [10] and [11].
 
Edge-assisted healthcare solutions transfer processes to the end-user which not only reduces the use of energy but also reduces response time. As a matter of fact, edge technology assists smart healthcare systems to mine and then process health information collected through edge services and devices that are near to the user. Additionally, processing information with edge also helps in boosting security, mobility, privacy, geographical distribution, lower network bandwidth use as well as location awareness along with facilitating on the web diagnosis and analytics thereby, reducing hospital visits [2]. Therefore, edge technology greatly contributes towards the development of a smart healthcare systems by delivering swift, comprehensive as well as universal treatments.
 
IoT Healthcare
 
Fig. 1: Edge-assisted smart healthcare functionality flow.
 
The architecture of edge computing in smart healthcare services comprises four distinct layers including sensor layers, edge devices, edge servers, and cloud data centers. This architecture has been illustrated in Figure 1 in the form of functionality flow, representing the functions as well as the common devices that are being used at every layer. In the sensor layer, there are numerous sensors including both implanted as well as wearable sensors which have the function of obtaining health data and vital signs from the patients. The figure indicates the type of data which can be collected with the help of these sensors. This layer initiates communication with edge devices layer by means of low power and short-range wireless communicating protocols like Bluetooth, ZigBee, and Wi-Fi [10]. This layer includes users’ devices like smartphones, smartwatches, tablets, and raspberry pi which have the capability of conducting various processes as well as storing data in the shorter term.
 
The information in the edge devices layer is transmitted to the edge server layer through Wi-Fi, Bluetooth, or internet protocols. In the edge server layer, there are microdata centers through which it conducts numerous processes and stores more data against the edge devices layer [11]. Figure 1 illustrates that the functionality of the edge devices layer and edge servers’ layer is nearly the same except that the computational power and storage capacity in the edge server layer is a lot more powerful than that of the edge devices layer. The final layer of the cloud data center incorporates highly powerful processors and has the capability to store huge amounts of data. This layer develops communication with edge servers’ layer over the internet.
 
Artificial Intelligence in Edge Assisted Smart Health Care Systems
 
Artificial Intelligence (AI) has excelled in numerous sectors which have ultimately opened up a vast amount of possibilities for machines to start ‘thinking’ and automate tasks given which was once the duty of humans so as to gain faster and more accurate results. In recent times, AI has penetrated itself in the medical sector as well. With the combination of edge AI with reliable and strong computer hardware, medical technology is now capable of incorporating deep learning so as to deliver better and improved patient care as well as medical outcomes of the patients.
 
 
Internet of Things (IoT) and Big Data have emerged as major players in the healthcare sector. In the future, this sector can highly benefit through AI assistance or might even take over some tasks that are currently assigned to doctors and healthcare staff a good example of this is a radiologist have a task to analyze ultrasounds so as to arrive at a diagnosis of the patients. However, radiologists can assess only a few of the medical images at a time. When this task of analysis is allocated to AI software, its computer hardware building block will have the capacity to analyze multiple images at a very swift pace. In addition to this, the vast amount of historical data stored within the AI server, the system will also be able to identify any abnormalities along with calculating results on its own. Another benefit from these systems is that they can also offer customized treatment plans based upon the medical history in the patients’ records.
 
When the AI technology in the healthcare sector is attained at a perfect pace, the sector will be more capable of improving and saving lives along with reducing costs. Additionally, the technology will also eliminate the risk of any human error within the diagnosis and provide accurate and reliable information which is crucial for expedited care and medical treatment. This technology also fosters clinical workflow and free up a lot of time of doctors and physicians which can be used for patient interaction and consultation along with reducing the time involved in receiving treatment.
 
artificial intelligence (AI) 
 
Opportunities and Challenges 
 
Healthcare is at the edge of a revolution – one which is driven by technologies such as artificial intelligence (AI) and the other one with edge computing. The implementation of artificial intelligence in healthcare is expected to grow at an annual growth rate of 41.4% from 2020 to reach $51.3 billion by 2027, while edge cloud computing is expected to grow by 34.1% between now and 2025. As both technologies are growing continuously, they are greatly being made part of the decisions made in the healthcare sector. The technologies go hand in hand – AI has become the key driver for edge computing and edge computing is a significant enable for AI.
 
 
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Transforming the rate of biomedical discoveries through Edge-AI
 
In this era of the COVID-19 pandemic, the healthcare sector has transformed towards AI for accelerating research over COVID-19 so as to discover more about the virus as well as being able to develop suitable antiviral medicines. A UK-based technological organization, Causaly, developed a biomedical research tool that allows researchers to carry out deep researches and find answers to complex research questions which would take weeks and even months otherwise. The AI of Causally can read, understand as well as interpret a huge database of biomedical information within seconds, allowing researchers to rapidly map epidemiology data, molecular targets, biomarker genes along with identifying potential treatment options [12].
 
These types of AI applications are traditionally powered by cloud computing and data centers. However, with time AI has become a lot closer to the user – within software and Internet of Medical Things (IoMT) endpoints as well as other medical devices. Examples can be wearable health devices like blood pressure monitors and ECG monitors which can collect and assess data locally and which can be easily shared by a patient to the doctor for carrying out an instant health assessment.
 
The result of this is that more and more healthcare businesses that are involved in AI have started to understand the importance of edge computing. As a matter of fact, Deloitte expects to sell more than 750 million edge AI chips which have been designed to allow on-device machine learning this year.
 
Transferring the Dull Work from Doctors to Machines
 
The use of edge-AI algorithms to support medical decision making, workflow management, and early diagnosis is relevant because the technology does not suffer from human deficits such as fatigue, its use would result in care more efficient for patients and time savings for healthcare professionals.
   
 
An average nurse in the US spends almost 25% of her time on administrative tasks. This can easily be reduced by the implementation of Robotics Process Automation. Moreover, Natural Language Processing (NLP)-based assistants like chatbots used to perform initial patient interviews are an off-the-top-of-the-head example. Developing a comprehensive Electronic Health Record System using edge computing technologies and powered by AI is the first step towards the development of the next generation Edge-AI powered healthcare solutions [13].
 
Administrative Workflow Management: Using edge-AI to automate administrative workflow through custom software development allows physicians to save time on routine tasks and prioritize issues urgent. Managing routine tasks, such as entering medical notes into patient charts, can be done using audio-to-text transcription software, saving valuable time.
 
The use of chatbots coupled with technology can also save caregivers precious time. The sculpin is able to communicate with patients, in order to follow them at home and trigger an alert in the event of an abnormal response. The caregiver can then take over, and devote his time to the patients for the patients who really need it [14].
 
Predictive analysis: Patient data collected from medical records and data obtained from connected objects give the doctor access to valuable information on the patient as well as on the population cohort to which the patient belongs. Computing this data using AI algorithms and edge computing helps develop the patient profile and build predictive models to effectively anticipate, diagnose and treat disease.
 
Prognosis: The massive use of data by AI makes it possible to improve the prognosis of patients by adapting the treatment to the characteristics of the disease and the specificities of each person. This precision medicine thanks to edge computing now offers the possibility of prescribing the best therapeutic options according to a very particular profile to maximize the chances of success of the treatment [15].
 
 
 
Providing Early Diagnostic Services
 
The proliferation of connected objects combined with the computing power of Edge-AI has enabled healthcare professionals to better monitor the patient and detect life-threatening risks at an earlier stage and easier to treat [16]. Some examples of applications of this data that are currently used in the health sector
 
Heart Condition Detection: Connected devices can be used to track the heart rate and monitor the patient’s EKG. This allows underlying heart conditions to be detected and diagnosed earlier. This is the case of the European MAESTRIA project, the objective of which is to prevent heart rhythm disorders and the risk of stroke, by integrating artificial intelligence. The mass of data from different medical sources can thus be analyzed in order to offer personalized therapies to each patient [17].
 
Breast cancer detection: AI is currently used to analyze mammograms. It was discovered that the scan rate is 30 times faster than that of a human and has an accuracy of 99%. It is able to detect extremely small metastases that humans are likely to miss. This not only reduces the risk of misdiagnosis but also reduces the need for invasive biopsies to make the diagnosis. This is the case with the company Mammoscreen, which uses AI and edge computing for the interpretation of screening mammograms [6].
 
Disease Trends: Patients are increasingly relying on search engines like Google to check their symptoms online before going to the doctor. Using AI to monitor this trend and draw conclusions can lead to early intervention in the event of a possible outbreak in the population. Google had already tried to do this in 2008 with influenza trends with its Google Flu Trends tool, but it failed due to the lack of streamlined data and numerous inconsistencies [18].
 
With advances in computing, this can now become an important asset for the early detection of infectious diseases and the prevention of their epidemics. From the end of December 2019, the Canadian company BlueDot had alerted to the coronavirus epidemic, even before the first alert from the World Health Organization, thanks to its AI technology.
 
Turning Electronic Medical Records into Risk Predictors: Patient medical records are a gold mine of data, but sorting them out and getting useful results are a task that would waste a lot of human time and effort. This is where the power of edge computing and AI comes in.
 
Improving Existing Processes by Providing Robotic Support
 
Like every institution, the healthcare sector is also prone to resource and time leakage. This can eventually result in various inefficiencies which stack up with a scale to huge amounts. An example of this can be mislabeled data that comes from radiology and histopathology diagnoses. These are usually associated with human errors as the specialist has done his best; however, the mistake is later on verified by the doctor. The best options can be to repeat the diagnosis process or ask for an update with the lab. All this involves a great deal of time.
 
A medical tool makes use of the Edge-AI system and aims in handling such issues maintaining focus over microscopic slides used in histopathology.
 
Robot-assisted surgery: Surgical robots use Edge-AI to use information from previous operations to improve surgical techniques. Data from preoperative records are integrated with operational measures to improve the outcome of the operation. These operations are minimally invasive and the precision of the robot-assisted instrument helps reduce the degree of post-op trauma.
 
Autonomous robotic surgeries: Although currently limited to science fiction, robotic operations may become a reality in the future. Using machine learning to combine motor pattern recognition and visual interpretation of data can extend surgeon’s dexterity to robots and make autonomous robotic surgery a reality. Robotic surgery is currently limited to the remote control of robots by the surgeon via a computer, but this could change in the future [19].
 
Auxiliary Robots: These are robots that find application in various fields, including patient care, nursing, and the care of the elderly and debilitated patients.
 
Chatbots: Chatbots are AI-powered algorithms capable of conducting conversations with patients. They have the potential to become the first point of contact for primary health care. The severity of the request is determined and chatbots can either fix the problem or pass it on to the doctor. The widespread use of chatbots considerably reduces the workload of the doctor and avoids having to go unnecessarily to health professionals. This is the case of the company Calmedica, which uses artificial intelligence to communicate by SMS with patients before and after a consultation and to improve their follow-up [20]
 
Edge Analytics in Healthcare Powers Improved Patient Outcomes
 
Edge analytics can help physicians take a more holistic approach to disease management, better coordinate care journeys, and ultimately help patients stick to their long-term treatment better. Technology also plays a vital role in delivering care through telemedicine and remote patient monitoring.
 
Here are Some of its Applications
 
Audio-to-Text Transcripts: Healthcare professionals spend a lot of time entering medical notes into patients’ medical records. Voice-to-text transcription of these notes using AI would increase the time spent on patient care.
 
Precision Medicine: Making relevant patient data available to physicians is a further step in the development of precision medicine. It allows physicians to make medical decisions tailored to each patient and create treatment plans specific to each.
 
Edge-AI has the ability to analyze a large selection of patient data (symptoms, lifestyle, treatments, etc.) in order to offer a very precise and reliable diagnosis. In this context, one of the major challenges is to guarantee the interoperability of the different data sources (medical files, connected objects, applications, etc.) in order to offer complete analyzes.
 
Applications of Edge-AI in the Diagnosis of Diseases
 
Diagnosis using x-rays: The use of edge computing and AI to analyze x-ray images obtained by MRI machines, scanners, and x-rays have not only made it possible to obtain a diagnosis comparable to that of a radiologist, but the results were also much faster [22]. The use of AI in diagnosis is meant to be an adjunct for the radiologist, who can use AI for routine cases and use his resources for more complicated cases.
   
 
For example, the start-up Incepto uses artificial intelligence in medical imaging, with the aim of rapidly detecting cancer or cardiovascular and neurodegenerative diseases. It allows doctors to cope with the exponential increase in health data to be analyzed.
 
Use of AI in Oncology: AI-infused edge computing can effectively predict tumor behavior by combining analysis of clinical, microscopic, and molecular data. This would allow doctors to better understand the behavior of tumors as a whole, to better define their aggressiveness, and thus to select the treatment that would give the best results [21].
 
Edge-AI Enabled Imaging
 
In the recent few years, Edge-AI has expanded at a drastic rate particularly in the fields of medical imaging and diagnostics. This way, these technologies have helped medical researchers as well as healthcare providers to offer clinical practice which is not only effective but, flawless as well [23]. Making way for standardization and quantification, deep learning assists in the prevention of errors that may occur in the process of diagnostics along with improving upon the outcomes of the test. In addition to this, AI in edge technology is improving assessments of medical imaging so as to detect cases of Diabetic Retinopathy (DR) and malignancy. It also helps in quantifying the flow of blood in the form of visuals.
 
As per a recent poll carried out by European Radiology Experimental, more than 50% of healthcare leaders across the globe consider the role of AI in edge technology to be promising in the process of diagnosing and monitoring medical images and diagnostics and this percentage will grow significantly.
 
Remote Care
 
Another promising area that is clearly associated with the combination of two cutting edge technologies – edge computing and AI is reliable remote healthcare. Developing a remote patient monitoring system that will allow access to health data through connecting various medical as well as non-medical devices can be only possible by the combination of the two technologies. The platform is designed to offer patient care services, patient health programs, and chronic disease management. in addition to this, it also enables continuous care of the patients and the elderly at home which will reduce their cost of treatment.
 
The benefits from edge computing-based remote care will be highly significant. A study carried out in 2015 indicates an almost 50 percent reduction in 30-day readmissions and up to a 19 percent reduction in the 180-day readmission amongst patients who received remote care. The bottom line stands to benefit as well with estimates suggesting that telemedicine alone could help cut U.S. employer healthcare costs by as much as $6 billion annually.
 
Key AI Technologies that has Transformed the Healthcare Sector
 
Technologies like Deep Learning, NLP, Intelligent Robotics, and Context-Aware Processing are considered to be the backbone of Edge-AI in the real world. These are the technologies that have transformed the healthcare sector to a greater extent.
 
Deep Learning
 
The process of diagnosing and treating diseases is more likely to improve by the implementation of AI within the healthcare sector. Deep Learning is an essential component in Edge-AI and can be used for analyzing medical images and data so as to increase the potential of physicians for giving effective treatment to patients. Through deep learning, visually challenged can develop a sense of their environment as the AI-computer vision and the text-to-speech apps will describe the text, detect facial cues of the people nearby, assess the surroundings, and give an explanation of the environment. Within the revolution of deep learning, three trends include powerful Graphics Processing Units (GPUs), sophisticated neural network algorithms modeled on the human brain, and access to a huge amount of data from the web [24].
 
Natural Language Processing (NLP)
 
Edge-AI also has a very important role to play in converting a whole lot of complex data within meaningful and simple information and this process is made easier through NLP. The focus of NLP is to mimic responses that are similar to human responses through using algorithms for responding to queries and conducting conversations. In the healthcare sector, NLP is used for summarizing long narrative text like clinical notes or academic journals through identifying key phrases or concepts. Additionally, NLP can also map out data elements within EHR that are present in the form of unstructured texts and transforming them into structured data which becomes meaningful and helps in improving clinical decision making [25].
 
Intelligent Robotics
 
Edge-AI is an integral component in robotics. Robots have the potential to revolutionize life-caring facilities by reducing the need for hospitalization and helping people to stay healthy. AI in alliance with advanced humanoid design will enable robots to carry out social interactions and conversations with elderly patients so as to keep the presence of mind intact. As robots can be integrated with a higher level of flexibility and reach, they can be easily used for smaller incisions with a lot of precision within the infected area. Additionally, robots can also be used as a social partner for treating mental health issues of patients [26].
 
Context-Aware Processing
 
Other applications of Edge-AI in the healthcare sector are virtual assistant applications like Google Assistant, Apple Siri, Microsoft Cortana, and Amazon Alexa in the medical sector which will carry out tasks as has been commanded by the programmer. AI chatbots, when applied in the healthcare department will significantly reduce the burden over healthcare providers to coordinate care as well as to detect issues and diagnose any health-related concerns. When speaking of the evolution of health assistants, bots are rendered to be the greatest invention up till now. However, chatbots are only enablers within this process to help patients in getting to the right physician for their treatment.
 
 
 
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Case Study
 
AI and Edge Computing – Histopathology Analysis at Nvidia Jetson
 
Microscopic slides can be defective in a variety of ways. The mistake can occur at the staining procedure was performed to make tissues clear or; it can be the improper sample collection process, any issues with the sample itself, or the sample have been marked by the pathologist with a pen.
 
These aspects tend to reduce the reliability of the diagnoses made both by humans and analysis by AI models.
 
The technology was provided from the Virtum platform which is a comprehensive toolset to assess and process large-scale images having focused on microscopic. Virtum, is a cloud-based tool however, some of its functions can be transferred to an edge computing device through making use of the Nvidia Jetson program.
 
About Nvidia Jetson Nano
 
Nvidia Jetson is a small operating system which is used for designing, developing and prototype embedded system for both IoT and edge healthcare devices identical. The device can be compared with solutions offered by Arduino or Raspberry Pi and is supported by Nvidia. The size of the computer can be compared with a coffee pack which means it can easily be made part of any setup or can be transported without any hassles. The technology is repeatable and there are no issues in adding the component anywhere.
 
Why Scan Quality Validation is Important?
 
Scans of histopathology require higher levels of proficiency to work with. However, despite the skills of people working, there are chances of multiple errors which can be visible in the slides.
 
  • regions containing ink
  • out of focus regions in the slide
  • torn or folded regions
  • tiling effect because of stitching
  • missed out regions during scanning
 
The development of AI in the healthcare department involves digitalization as well as the development of accessible as well as trustworthy datasets. The occurrence of any mistake listed above can result in the ineffectiveness of Ai automation and algorithms. In addition to this, a non-perfect slide within the dataset can impose a significant impact on the effectiveness of the entire network.
 
This solution will eventually help physicians to deliver accurate diagnoses as it will provide them information that is accurate and at the right time. Automated quality control after scanning helps in preventing clutter within the database and gives a chance of redo to the scanner or take any other adequate action if and when possible.
 
Gains
 
The device will greatly assist in the work of histopathology workers who are annotating slides. The device can be easily added up to any existing setup so as to gain insights into daily work or building new datasets. If the same device is adjusted with additional technologies, it can also be effectively used in radiology or similar other tasks that require marking over larger images.
   
 
The solution of the platform is that it can bring in considerable synergies when applied in conjunction with edge computing. Building a collection of properly marked annotated slides is very important in developing AI-based tools or automating any image-related workflow.
 
Summary
 
The solution of Nvidia edge AI-based shows how implementing single-step automation can result in the saving of both time and resources. In addition to this, it also shows that AI is the go-to strategy in the healthcare sector particularly when the aim is to save with bandwidth along with preparing better input for cloud-based solutions.
 
The Challenges of Adopting Edge-AI
 
The adoption of Edge-AI in healthcare opens up a number of possibilities, but it also comes with a series of challenges.
 
The phenomenon called “black box”
 
In some cases, AI systems offer solutions that cannot be explained. This phenomenon is explained by the complexity of these algorithms which perform a very large number of micro-reasonings that, placed end to end, allow the machine to issue its diagnosis. However, these are so numerous that it is impossible for a human to understand them, hence the black box.
 
However, to be acceptable, the decisions of the algorithm must be able to be understood and therefore explained. But in the case of the black box, the number of micro-reasonings performed by the machine is such that it is not possible to understand them [26].
 
Complexity of stakeholders
 
All players in the healthcare sector, including patients, healthcare professionals, pharmaceutical companies, hospitals, insurance companies, are stakeholders in the adoption of AI. Resistance to technology at any level would lead to problems in integrating the technology as a whole. As with any new technology, there is an initial reluctance to adopt it in the marketplace, with healthcare facilities and users concerned about its application and safety [27].
 
Compliance with regulations
 
According to the report “Giving a meaning to artificial intelligence” by Deputy Cédric Villani published in March 2018, artificial intelligence “opens up new opportunities to innovate” with constant pharmacopeia “by building a diagnosis and a therapeutic strategy more suited to the need. of the patient, his environment and his way of life ”. Bringing hope, artificial intelligence also generates several legal challenges [28].
 
AI can only develop with very large volumes of data. In France, the collection and use of health data are subject to a number of laws such as the GDPR and the incorporation of AI is subject to the approval of organizations to ensure that standards are maintained [29]. The challenge for political and legal institutions will be to allow the development of the interoperability of information systems within a protective legislative framework and to find the right balance between the use, access, and security of health data. The question is not to produce more data but, above all, to pool those that already exist, and to facilitate access within an ethical and protective framework [30].
 
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References
 
[1] A. Abdel latif, A. Mohamed, C. F. Chiasserini, M. Tlili, and A. Erbad, “Edge computing for smart health: Context-aware approaches, opportunities, and challenges, ”IEEE Network, vol. 33,no. 3, pp. 196–203, 2019.
 
[2] S. Rani, S. H. Ahmed, and S. C. Shah, “Smart health: a novel paradigm to control the chickungunya virus, ”IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1306–1311, 2018
 
[3] Bierzynski K., Escobar A., Eberl M. 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC) IEEE; 2017. Cloud, fog and edge: cooperation for the future?
 
[4] Azimi I., Anzanpour A., Rahmani A.M., Pahikkala T., Levorato M., Liljeberg P., Dutt N. HiCH: hierarchical fog-assisted computing architecture for healthcare IoT. ACM Trans. Embed. Comput. Syst. (TECS) 2017;16(5s):174.
 
[5] Chiang M., Zhang T. Fog and IoT: an overview of research opportunities. IEEE Internet Things J. Dec. 2016;3(6):854–864
 
[6] Ibrahim W.N.H., Selamat A., Krejcar O., CHAUDHRY J. New Trends in Intelligent Software Methodologies, Tools and Techniques. IOS Press; 2018. Recent advances on fog health – a systematic literature review.
 
[7] Uddin M.Z. A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system. J. Parallel Distrib. Comput. 2019;123:46–53
 
[8] Hartmann M., Hashmi U.S., Imran A. Edge computing in smart health care systems: review, challenges, and research directions. Trans. Emerg. Telecommun. Technol. 2019;e3710
   
 
[9] Azimi I., Takalo-Mattila J., Anzanpour A., Rahmani A.M., Soininen J.P., Liljeberg P. 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) IEEE; 2018. Empowering healthcare IoT systems with hierarchical edge-based deep learning; pp. 63–68.
 
[10] J. Azar, A. Makhoul, M. Barhamgi, and R. Couturier, “An energyefficient iot data compression approach for edge machine learn-ing,”Future Generation Computer Systems, vol. 96, 2019.
 
[11] Abdellatif A.A., Mohamed A., Chiasserini C.F., Tlili M., Erbad A. Edge computing for smart health: context-aware approaches, opportunities, and challenges. IEEE Netw. 2019;33(3):196–203
 
[12] Bishop,C.M.(2013)Model-basedmachinelearning.  Philos.Trans.AMath.Phys.Eng.Sci. 371http://dx.doi.org/10.1098/rsta.2012.0222
 
[13] Ahmed Abdulkadir Ibrahim, Yasin Muhamma, Wang Zhuopeng,” “ IOT Patient Health Monitoring System” Int. Journal of Engineering Research and Application ISSN: 2248-9622, Vol. 7, Issue 10, October 2017, pp.01-03
 
[14] Musa, M. Opinion: Rise of the Robot Radiologists.Science. 2018. Available online: https://www.the-scientist.com/news-opinion/opinion–rise-of-the-robot-radiologists-64356 (accessed on 15 November 2020).
 
[15] Palanica, A.; Flaschner, P.; Thommandram, A.; Li, M.; Fossat, Y. Physicians’ Perceptions of Chatbots in Health Care: Cross-sectionalWeb-based Survey.J. Med Internet Res.2019,21, e12887
 
[16] Rohini Raina, Dr. Naveen Kumar Gondhi, “Machine Learning Techniques in IoT” International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 4 | Issue 1 | ISSN: 2456-3307
 
[17] A.Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari and M. Ayyash, “Enabling Technology for the internet of Health Things”. IEEE Translation and content mining, vol. 17, pp. 2347 – 2376, 2015
 
[18] Shubham Banka, Isha Madan and S.S. Saranya “Smart Healthcare Monitoring using IoT” International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 15 (2018) pp. 11984-11989
 
[19] Yoon, S.; Lee, D. Artificial Intelligence and Robots in Healthcare: What are the Success Factors for Technology-based ServiceEncounters?Int. J. Healthc. Manag.2019,12, 218–225
 
[20] Ramesh, A.; Kambhampati, C.; Monson, J.; Drew, P. Artificial Intelligence in Medicine.Ann. R. Coll. Surg. Engl.2004,86, 334–338.
 
[21 Lee, S.; Lim, S.Living Innovation: From Value Creation to the Greater Good; Emerald Publishing Limited: Bingley, UK, 2018.
 
[22] Safavi, K.; Kalis, B. How AI can Change the Future of Health Care.Harv. Bus. Rev.2019. Available online: https://hbr.org/webinar/2019/02/how-ai-can-change-the-future-of-health-care (accessed on 15 June 2020).
 
[23] Magaña Espinoza P., Aquino-Santos R., Cárdenas-Benitez N., Aguilar-Velasco J., Buenrostro-Segura C., Edwards-Block A. WiSPH: a wireless sensor network-based home care monitoring system. Sensors. 2014;14(4):7096–7119
 
[24] LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning.Nature2015,251, 434–444.
 
[25] Sulleyman, A. Stephen Hawking Warns Artificial Intelligence ‘May Replace Humans Altogether’. 2017. Available online:https://www.independent.co.uk/life-style/gadgets-and-tech/news/stephen-hawking-artificial-intelligence-fears-ai-will-replace-humans-virus-life-a8034341.html (accessed on 15 October 2020).
 
[26] Capobianco G, Di Giacomo U, Martinelli F, et al. Wearable Devices for Human Activity Recognition and User Detection[C]//2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2019: 365-372
 
[27] Yoon; Lee, D. Effects of Key Value Co-creation Elements in the Healthcare System: Focusing on Technology Applications.Serv. Bus.2019,13, 389–417
 
[28] Rigby, M. Ethical Dimensions of Using Artificial Intelligence in Healthcare.AMA J. Ethics2019,21, E121–E124
 
[29] Lupton, M. Some Ethical and Legal Consequences of the Application of Artificial Intelligence in the Field of Medicine.TrendsMed.2018,18, 1–7.
 
[30] Luxton, D. Artificial Intelligence in Psychological Practice: Current and Future Applications and Implications.Prof. Psychol. Res.Pract.2014,45, 332–339
 
 
 
 
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