Artificial intelligence in medicine (projeck)

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Artificial intelligence in medicine
Advisor : M. ibrhim Al-edane
Name : Yasser Ali Al-muwallad
ID : 441147871
Name : Ali Mohammad Shrahili
ID : 441147863
Name : Emad Abdullah Al-hadr
ID : 441147867
Name : Khaled Abdullah Al-shamrani
ID : 441147912

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Indexing :
Introduction 3
Understanding AI 4
Diagnostics and disease detection 5
How do we use AI in medicine? 6
Scientific tests and experiments 7
Some AI systems use logic ... 8
Others use past experience 9
Surgery robots 9
Cancer detection 13
Robotics in full expansion 18
The challenges of research 18
A bottleneck: the quality of the data
sample
19
Provide information at the right time
and at the right level
19
Provide real help to medical practice 21
Provide the means to understand the
decision
22
Help the doctor and not replace him 24
Cognitive science: source of
inspiration and field of application
25
List of references 26

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➢ Introduction
In light of the continuous acceleration of investments related to artificial intelligence
technologies, and the intense competition between major international companies to
transform these technologies into tools and applications for personal and commercial use, it
seems that the health sector has already become one of the first sectors to benefit from this
progress. It is no longer just a guesswork, it has become a highly sophisticated and complex
field.
Artificial intelligence has made great progress in the health field, as technologies have
facilitated the treatment of many problems related to filling registration forms and the
outbreak of discounts at reception offices, as well as developing the health care system in a
more effective and efficient way to detect diseases, in addition to the development of robots
that are now performing surgeries and diagnostics. flour.
During the past recent years, artificial intelligence has made great progress, as indications
show that it has already reached the stage of being able to provide real solutions to health
care problems, which foretells a medical revolution against the prevailing old rules.
The issues of artificial intelligence techniques mainly revolve around simulating human
capabilities such as logical thinking and learning, and on the question of whether it excels in
its ability to analyze big data and reach accurate scientific conclusions during record periods.
AI has many uses in the healthcare sector, from diagnosis and drug development to hospital
workflow management. Perhaps the most

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prominent services of artificial intelligence for the medical sector are summarized in five
uses.
Artificial intelligence (AI) is a rapidly expanding field of research with a great future. Its
applications, which concern all human activities, make it possible in particular to improve the
quality of care. AI is indeed at the heart of the medicine of the future, with assisted
operations, remote patient monitoring, smart prostheses, personalized treatments thanks to
the cross-checking of a growing number of data (big data), etc.
Researchers are developing multiple approaches and techniques for this, from language
processing and ontology construction, to data mining and machine learning. However, it is
essential for the general public to understand how these systems work in order to know what
they are doing and especially what they are not doing. The omniscient robot, which for many
symbolizes AI, is not for tomorrow!

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I. Understanding AI
Artificial intelligence was born in the 1950s with the objective of having human tasks
produced by machines that mimic the activity of the brain. Faced with the setbacks of the first
hours, two currents were formed.
The proponents of so-called strong artificial intelligence aim to design a machine capable of
reasoning like humans, with the supposed risk of generating a machine superior to humans
and endowed with a consciousness of its own. This line of research is still being explored
today, even though many AI researchers believe that such a goal is impossible.1
On the other hand, proponents of so-called weak artificial intelligence are using all available
technologies to design machines capable of helping humans in their tasks. This field of
research mobilizes many disciplines, from computer science to cognitive sciences through
mathematics, without forgetting the specialized knowledge of the fields to which one wishes
to apply it. This approach - which will be discussed throughout this file - generates all the
specialized and efficient systems that inhabit our environment today: creating profiles of
possible friends on social networks, identifying dates in the texts to classify agency
dispatches, helping the doctor to make decisions, etc. These systems, which vary in
complexity, have in common that they are limited in their adaptability: they must be manually
adapted to accomplish tasks other than those for which they were originally designed.
1 Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1993). DENDRAL: a case study of the first expert system for scientific
hypothesis formation. Artificial intelligence, 61(2), 209-261.

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➢ So, what is artificial intelligence in medicine?
Artificial intelligence (AI) is a technology inspired by neural networks in the brain. It uses
multiple layers of information (algorithms, pattern matches, rules, deep learning, and
cognitive computing) to learn to understand data.2
II. Diagnostics and disease detection
A report issued by the MIT Review website, which specializes in technologies, monitored
these uses, as artificial intelligence has recently made remarkable progress in the field of
detecting diseases in their early stages. For example, a scientific paper published last year
reported that a deep-learning system was able to diagnose esophageal cancer with an
accuracy of 98 percent, even though diagnosing this type of cancer is relatively difficult, often
at an advanced stage when the opportunity for treatment is missed. effective.
Many wearable applications and tools also use artificial intelligence techniques that monitor
disturbances in the body's vital signs and can predict the possibility of a health crisis before it
occurs. The US platform CarePredict has developed a wearable tool that tracks even small
changes in older people's behavioral patterns that precede falls, malnutrition and depression,
and can send out rapid distress signals when needed.3
2 Clancey, W. J., & Shortliffe, E. H. (1984). Readings in medical artificial intelligence: the first decade. Addison-Wesley Longman Publishing
Co., Inc.
3 Ibid.

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The field of precision medicine depends on identifying the most effective drug for patients
based on their genetic makeup, lifestyle, and drug response. In this context, studies
demonstrate a clear positive impact
when physicians’ work is integrated with artificial intelligence in order to pave the way for
precision medicine, where deep learning techniques can analyze genetic data from large
numbers of individuals, identify individual variation in response to drugs, and support clinical
decision-making in time. Actual, and thus make recommendations about the most
appropriate drug for each person.
In recent years, projects that collect and analyze huge health data using artificial intelligence,
with the aim of developing the field of precision medicine, have increased. In addition to the
“British Biobank” project and the “Digital Model from You” project in China, the United States
launched in 2019 the “All of Us” project, which Aiming to enroll one million individuals,
participants provided a huge set of information, including health records, genetic data, as
well as data recorded by personal activity trackers.4
III. How do we use AI in medicine?
AI tools can identify meaningful relationships in raw data. These tools can be used in almost
any area of medicine: drug development, treatment decisions, patient care, and financial and
operational decisions.
4 Miller, R. A. (1994). Medical diagnostic decision support systems—past, present, and future. Journal of the American Medical Informatics
Association, 1(1), 8-27.

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Thanks to AI, healthcare professionals can tackle complex problems that would be difficult,
time-consuming and inefficient to tackle otherwise. AI can be a valuable resource for these
professionals by enabling them to make better use of their expertise. AI can bring value to
the entire healthcare ecosystem.
IV. Scientific tests and experiments
A recent analysis shows that the main reasons for failure of up to 88 percent of drug-
development trials in the United States are lack of funds, participants dropping out of trials, or
failure to recruit enough volunteers; Therefore, researchers have turned to using data
collected by artificial intelligence technologies from electronic health records and wearable
devices, as they can save billions of dollars, and they also have large amounts of data that
can be analyzed. Furthermore, these technologies allow algorithms to search medical reports
for people who are eligible to participate in clinical trials.
In another context, researchers used a deep learning algorithm called “Extreme Gradient
Posting” to explore new ways to confront “superbugs,” which are species that are resistant to
most or all antibiotics, as this algorithm was able to determine the ability of bacteria strains to
resist drugs with an accuracy of 95 percent.5
It remains to say that with the tremendous technological progress and the development of
more and more intelligent technologies, many - including health care workers - fear that
robots and artificial intelligence systems will destroy their jobs, but current indicators clearly
indicate that
5 Dinov, I. D. (2016). Volume and value of big healthcare data. Journal of medical statistics and informatics, 4.

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the integration between human intelligence and artificial intelligence is The best way to
achieve the next revolution in the health sector.
V. Some AI systems use logic ...
The oldest approach is based on the idea that we reason by applying logical rules
(deduction, classification, prioritization, etc.). The systems
designed on this principle apply different methods,6
based on the development of interaction
models between automata or autonomous software (multi-agent systems), syntactic and
linguistic models (automatic language processing) or the development of ontologies.
(knowledge representation). These models are then used by logical reasoning systems to
produce new facts.
In the 1980s, this so-called symbolic approach allowed the development of tools capable of
reproducing the cognitive mechanisms of an expert. This is why they have been called
"expert systems". The most famous, Mycin (identification of bacterial infections) or Sphinx
(detection of jaundice), are based on all the medical knowledge in a given field and a
formalization of the reasoning of specialists who link this knowledge together to achieve to a
diagnosis.
Current systems, referred to as decision support, knowledge management or e-health, are
more sophisticated. They benefit from better reasoning models as well as better techniques
for describing medical knowledge, patients and medical acts. Algorithmic mechanics are
basically the same, but description languages are more efficient and machines more
powerful. They no longer seek to replace the doctor, but to support him in reasoning based
on the medical knowledge of his specialty.
6 Dougherty, G. (2009). Digital image processing for medical applications. Cambridge University Press.

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The main difficulty of the symbolic approach is the modeling of knowledge (description of the
field and reasoning) which is based on in-depth work with specialists in the field concerned.7
VI. Others use past experience
7 Jasmine, D. (2014). Digital image processing for medical applications. Cambridge University Press.
Help with the management of breast cancer
Teams from the Medical Informatics and Knowledge Engineering
Laboratory in e-health (LIMICS, Inserm unit 1142) and from the
Assistance Publique - Hôpitaux de Paris, are participating in a
European project, Desiree, which is based on symbolic approach to
help clinicians in the treatment and follow-up of breast cancer
patients. These very complex diseases often require adaptations of
conventional protocols.
The Desiree platform integrates best practice recommendations
through the implementation of reasoning based on an ontology. The
system can also learn from cases already resolved (reproduction of
decisions made for cases similar to the clinical case to be resolved),
or from reasoning by experience (reuse of decisions that were not in
accordance with recommendations, on the basis of criteria explained
in the justification for not following up on recommendations). The
continuous enrichment of the case base allows the system's
proposals to evolve to help with the therapeutic management of
patients.

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✓ Surgery robots
Robots are the most imaginative machines in the world of artificial intelligence, and although
it may seem premature to talk about robots performing surgery, given that this is achieved in
the distant future, the imagination has become a reality. In 2017, a Chinese robot passed the
exam for practicing the profession in the country Using only artificial intelligence capabilities.
And last February, the surgical robot Verseus - manufactured by the British medical
technology company "CMR Surgical", and equipped with several arms that can assist
surgeons in operating rooms - was able to perform its first micro-surgical operations in the
specialty of colorectal surgery.8
In turn, the Korea Telecommunications Corporation "KT Corporation" announced last
January that it had developed, in partnership with the Samsung Medical Center, an initiative
to launch an innovative medical service, in an initial step to establish a hospital based on fifth
generation networks. The new service includes relying on delivery robots in operating rooms
to bring surgical supplies and remove contaminated materials and medical waste.9
8 Koomey, J., Berard, S., Sanchez, M., & Wong, H. (2011). Implications of historical trends in the electrical efficiency of computing. IEEE
Annals of the History of Computing, 33(3), 46-54.
9 Ibid.

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It also announced a new artificial intelligence system called “Smart Care Giver” that will
enable patients to control their rooms inside the hospital using voice commands, and check
their health status, thus enabling the medical staff to respond more efficiently to emergency
situations.One of the chronic problems facing the health care sector - even in the most
developed countries - is overcrowding in medical centers and hospitals, and the pressures
on doctors and nurses; In the United States, for example, a 2016 study revealed that 96
percent of patient complaints revolved around confusion in filling out registration forms and
poor experiences at reception desks. Therefore, a number of applications and systems
based on artificial intelligence have tried to find solutions to these problems.10
One such system is Olive, which has been specifically designed to automate repetitive tasks
in the healthcare sector, such as indexing insurance policies and submitting treatment
requests, so that workers can focus on more complex tasks and provide better patient
service. According to reports published last month, about 600 hospitals in the United States
are now relying on this system to automate the work of human resources, finance and supply
chain departments.
Some of these systems use artificial intelligence to enter data, perform analyzes, x-rays, and
other basic tasks. It is also sometimes used to analyze entire health care systems; For
example, in the Netherlands, digital healthcare bills accounted for 97% of all medical bills in
2017.11
10 "Artificial Intelligence in Radiology: The Game-Changer on Everyone's Mind". Radiology Business 06 ‫في‬ ‫األصل‬ ‫من‬ ‫مؤرشف‬ .)‫اإلنجليزية‬ ‫(باللغة‬
‫مارس‬
2019
‫بتاريخ‬ ‫عليه‬ ‫اطلع‬ .
10
‫أبريل‬
2018 .
11 Ibid.

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Because it contains the data for the treatment, the doctor and the hospital, the Dutch
company “Zorgbrisma Public” uses the “IBM Watson” system to analyze it, with the aim of
knowing whether the doctor, clinic or hospital is making repeated mistakes in treating a
particular type of case or not, and determining Reasons for inefficient workflow, and even
help in reducing unnecessary visits to hospitals.
✓ Cancer detection
During 2019, researchers at Northwestern University in Illinois, in the United States, in
cooperation with Google and many medical centers, worked on studying the data used with
the consent of thousands of cancer patients and determining whether the device diagnosis
was similar to what doctors found or is better and more accurate.
This technology is still under development and is not ready for widespread use, but the new
report, published in the journal Nature Medicine and reported by the New York Times,
provides a glimpse into the future of artificial intelligence in medicine.
Pattern recognition and image interpretation - two skills humans use to read microscope
slides, X-rays, MRIs and other medical examinations - are among the most promising fields
of science.12
Researchers can train computers to recognize patterns associated with a specific condition,
such as pneumonia, cancer, or a broken wrist that is difficult for a person to see, by collecting
massive amounts of data enabled by medical imaging and transferring it to systems called
“artificial neural networks.” From there, the system follows an algorithm
12 https://www.ibm.com/fr-fr/watson-health/learn/artificial-intelligence-medicine

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or a set of instructions and information, and the larger the amount of data, the easier the
interpretation.
This process, known as deep learning, is already used in many applications, such as
enabling computers to understand speech and
identify objects, so that a self-driving car recognizes a stop sign and distinguishes between a
pedestrian and a telephone pole. In medicine, Google has already built systems to help
pathologists read microscope slides to diagnose cancer, and to help ophthalmologists detect
eye diseases in people with diabetes.
In the new study, researchers applied artificial intelligence to CT scans used to screen
people for lung cancer, which killed 160,000 people in the United States last year and killed
1.7 million people worldwide. It was recommended to adopt radiological examinations in
cases of people at imminent risk due to their long-term smoking.
Studies have found that screening can reduce the risk of dying from lung cancer. In addition
to finding specific cancers, the scans can also identify spots that may later become cancer,
so radiologists can sort patients into groups with varying levels of risk, and decide whether
they need to examine the tissue of the skin or do ongoing exams to track Areas likely to be
affected. But researchers say the imaging test is not without its drawbacks. It may fail to
detect tumors or misdiagnose benign spots for malignant tumors and push patients to risky
procedures such as dermatological lung exams or surgery. Radiologists looking at the same
scan may have different opinions on this topic.13
13 Bloch-Budzier, Sarah. "NHS Using Google Technology to Treat Patients." BBC News, November 22, 2016.
https://www.bbc.com/news/health-38055509. ‫محفوظة‬ ‫نسخة‬
2020
-
03
-
29
‫مشين‬ ‫باك‬ ‫واي‬ ‫موقع‬ ‫على‬ .

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Fundus. This fundus shows the distribution of clotting points proposed by laser control software to treat
diabetic retinopathy. AI can automatically identify patients who should be eligible for such a procedure. ©
National Eye Institute, NIH.14
Deep learning applications exist in image processing, for example to identify possible
melanomas on photos of skin, or to screen for diabetic retinopathies on images of retinas.
Their development requires large training samples: 50,000 images in the case of melanoma,
and 128,000 in the case of retinopathies, were required to train the algorithm to identify signs
of pathologies. For each of these images, he is told whether or not it presents pathological
signs. At the end of the training, the algorithm is able to recognize with excellent performance
new images with an anomaly.
14 https://www.ipi-ecoles.com/intelligence-artificielle/

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✓ Robotics in full expansion
Robotics is a specific subfield of AI. It aims to increase the autonomy of machines by
endowing them with perceptual, decision-making and action capacities.
Computer-assisted surgery is undoubtedly one of the most famous aspects. It now makes it
possible to improve the precision of gestures or to operate from a distance.
Smart prostheses aim to repair or even increase the human body: artificial limbs or organs
(arm, cochlea, heart, sphincter, etc.), cardiac simulator, etc.15
Robots to assist people, the elderly or the frail, for example, represent a third highly
publicized and rapidly developing sector. This service robotics aims to imitate living things
and interact with humans. It raises many ethical issues, including the protection of privacy
and personal data, but also the consequences of blurring the human-robot border. A border
that can be quickly crossed by the user.
VII. The challenges of research
AI is booming and many research avenues are being explored to improve the technical
performance of these systems, but also their suitability for targeted medical practices.16
Their
cost must also be justified by a real added value for the doctor or the patient.
The lines of research focus in particular on the processing of data, which is very
heterogeneous, its structuring and anonymization, but also on the
15 https://blog.mbadmb.com/lintelligence-artificielle-au-service-du-diagnostic-medical/
16 Ibid.

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design of transparent systems for the user and well suited to the context of use.
✓ A bottleneck: the quality of the data sample
The digital approach can boast great performance in medicine, but it requires perfectly clean
and well-annotated data, such as that used for the recognition of melanomas. However, most
of the medical data has not been collected for the software developer’s goal. They therefore
pose many problems for their exploitation.
France has, in particular, one of the largest health databases in the world: its national system
of medico-administrative data, SNIIRAM (for National Health Insurance Inter-Regime
Information System). This database stores all drug prescriptions, disease descriptions and
hospital acts. However, it is tricky to exploit, as the basis was created for the economic
analysis of health services and not for medical analysis. Thus, a person hospitalized for a
respiratory problem will be treated for this problem without necessarily mentioning the cancer
which affects them elsewhere. In some cases, there are 30% errors in the description of
pathologies associated with patients. Correcting these errors involves crossing the data with
other sources, such as those corresponding to the drugs administered.17
✓ Protect personal data
A national health platform bringing together all the health data of the population is an
invaluable resource for practitioners, but also for medical
17 Power B (19 March 2015). "Artificial Intelligence Is Almost Ready for Business". Harvard Business Review. Massachusetts General
Hospital.

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and pharmaceutical research. However, we must ensure that this data is used wisely and in
accordance with the laws, in particular the General
Data Protection Regulation (GDPR) which entered into force in May 2018 and the law for a
digital republic of 2016.
In this context, personal data is not the property of the patient, nor of the body that collects it.
The French are the usufructuaries of their data: they can dispose of it but not sell it. On the
other hand, the processing of this data is conditional on the informed consent of the data
subject. In France, health data is anonymized to be accessible by researchers, only on
authorized projects.18
Cross-reference multiple textual data relating to patients
Another problem raised by the use of medical data, 80% of patient information is textual
(hospital reports or imaging reports, for example). It is then a matter of implementing
automatic language processing software to analyze these texts and extract information from
them patient training (data mining).
This software can mobilize a symbolic approach or approaches based on neural networks.
Unsupervised learning algorithms (without prior learning on samples) are raising hopes in
this field: they make it possible to quickly cross-check a very large number of data in order to
establish hidden structures and determine categories of interest. for the intended task. In this
way, we hope to be able to better identify risk factors,
18 Kobie N (1 January 2020). "DeepMind's new AI can spot breast cancer just as well as your doctor". Wired UK. Wired. Retrieved 1 January
2020.

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personalize treatments and verify their effectiveness, predict epidemics or improve
pharmacovigilance.
These algorithms can be very powerful but still require a lot of research before they can be
used reliably.
✓ Provide information at the right time and at the right level
In recent years, more targeted projects in their objectives have materialized. For example, in
2010, LIMICS researchers participated in the design of automatic language processing
software as part of the Lerudi project (for Rapid reading in emergency of the patient's
computer file). They piloted the development of the emergency ontology that goes into the
development of a prototype search engine for the patient's medical file or the future shared
medical file of the CNAM. Intended for emergency physicians, the tool must meet their
needs, in this case, bring to their attention essential information (such as drug prescriptions
that identify pre-existing pathologies) within the few minutes they have to make a decision. 19
In addition, a decision support system in ultrasound analysis for ectopic pregnancies (GEU)
developed by LIMICS and the Trousseau hospital, OPPIO, is entering the testing phase in
2019. It is supported by a ontology which provides a model centered on the signs of the
domain, with the relationships between the signs of the different types of ectopic pregnancy,
the anatomical structures and the technical elements. This
19 https://www.foreseemed.com/artificial-intelligence-in-
healthcare#:~:text=A%20common%20use%20of%20artificial,and%20better%20results%20for%20patients.

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system allows the doctor to select a type of GEU and to be offered the relevant signs to look
for and the associated reference images.
✓ Provide real help to medical practice
For an application to be used by the physician in his daily practice, it is not enough that it
render the service requested of it, the system must also be convenient! For example, a
system designed to alert on possible drug contraindications must not saturate the practitioner
with "correct" alerts, but not adapted to the patient's clinical context. Thus, instead of giving
an alert each time a contraindication arises, the new interfaces ask questions about the
patient upstream, in order to reduce the number of alerts and, thus, the tendency of the
doctor to disconnect a "intrusive" machine.20
✓ Provide the means to understand the decision
To be acceptable or legitimate, or even to be dismissed as deemed irrelevant, the decisions
of the algorithm must be able to be understood, and therefore explained. A major advantage
of symbolic approaches is that they allow the path of reasoning to be traced. But even then,
the number of micro-reasonings performed by the machine is such that it is unthinkable to
display them all. This is why researchers are currently working on how to describe this
reasoning "in explicit classes", in order to highlight the most important decisions.21
Only a
good understanding of the solutions offered by the application can indeed allow the doctor to
discuss with his patient and explain the possible alternatives.
20 Thomas Davenport,Ravi Kalakota, The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun; 6(2): 94–98.PMCID:
PMC6616181. PMID: 31363513
21 Ibid.

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Numerical approaches, on the other hand, are like a black box, incapable of justifying its
decisions: no one knows what the algorithm does. How, then, to take responsibility for the
medical decision? The learning data is in particular biased by the prejudices of the time and
those of the designers. The algorithm therefore tends to reproduce, even reinforce, these
same prejudices. In the medical field, the main biases are due to the over-representation of a
category of people, such as the elderly or patients of particular geographical origin.
The challenge for the future is to combine the approaches
Projects attempt to combine symbolic and learning approaches, in order to benefit from both
the reasoning of one and the performance of the other. Thus, in the Lerudi project cited
above, the construction of ontologies (symbolic AI) is made from digital text mining
algorithms.
Another example, the interpretation of pediatric medical images is of major importance for
diagnosis, patient follow-up or even preparation for surgery. It is about detecting, segmenting
and recognizing normal and pathological anatomical structures, and providing 3D
visualizations. To respond to the difficulty of these tasks, it is important to combine the digital
information extracted from the images, therefore specific to the patient, with generic models,
representing anatomical knowledge in the form of knowledge bases, ontologies, graphics,
etc.22
This is particularly crucial with pediatric images which must be acquired over as short a
time as possible and which show structures that are often small in size and vary widely from
patient to patient.
22 Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, Yongjun Wang, Artificial intelligence in
healthcare: past, present and future

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This dual approach is also particularly relevant for making use of “varied” patient data
(genomics, clinical, imaging and biological analyzes) which will be brought together on a
single platform within the framework of the France 2025 Genomic Medicine Plan.23
AI will
make it possible to manage this considerable amount of data by providing classifications or
descriptive ontologies of the clinical elements of patients. Machine learning will identify
patient profiles that take all of this data into account. It will then be possible to personalize
care and improve its success rate, in particular, initially, for cancers, rare diseases and
diabetes.
✓ Help the doctor and not replace him
Some see the medical applications of AI as an opportunity to replace the doctor, whether to
alleviate medical deserts or to screen patients and refer them. But public use of such
software without medical supervision raises important ethical questions. The system reduces
the relationship with the doctor to a technical act. He leaves the patient to his questions and
his anxieties.24
Moreover, the risk that the doctor will abdicate in front of the machine "who knows better than
him" is real. He may have to endorse a decision that is not his own and find out after the fact
that the machine was wrong. To avoid this pitfall, the doctor, the only one authorized to make
a diagnosis, must be able to maintain his autonomy in the face of the machine. He must be
able to understand the why and the how of the decisions posted, and to circumvent them if
necessary.
23 https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health/transforming-healthcare.html
24 Ibid.

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To this end, the Allistene (Cerna) committee on the ethics of research in digital science and
technology underlines the need to design systems whose operation is transparent, explicit
and traceable, and which perform the tasks specified in respecting explicit constraints. For
decision support systems based on learning algorithms, compliance with this compliance is
not obvious.
✓ Cognitive science: source of inspiration and field of application
Despite the enormous computing capacities offered by today's computers, no existing
application can claim to be truly intelligent: it would have to be multitasking and capable of
reacting correctly in unforeseeable and non-preprogrammed situations. We are still very far
from the target.25
To progress in this direction, researchers are trying to understand the behavior of neurons
and their connections, in order to be able to mimic the brain. This may one day create robots
that mimic human intelligence. In the meantime, it will help to better understand the
functioning of this organ and to better understand the causes. This is the goal that motivates
the participation of the European Union, as part of its flagship initiative Future and Emerging
Technologies, in the Human brain project. This project aims to build a world-class IT
infrastructure, which can be used by the scientific community to simulate brain function under
specific experimental conditions.26
25 https://givingcompass.org/article/artificial-intelligence-and-the-future-of-healthcare/
gclid=CjwKCAiAtdGNBhAmEiwAWxGcUgrfgzG_geudv4ty1qwq9AS3VEGujCB7sQ8SQH7S-qFW97K5ufC1nRoCU64QAvD_BwE
26 Ibid.

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References
▪ Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1993). DENDRAL: a case
study of the first expert system for scientific hypothesis formation. Artificial intelligence, 61(2), 209-
261.
▪ Clancey, W. J., & Shortliffe, E. H. (1984). Readings in medical artificial intelligence: the first
decade. Addison-Wesley Longman Publishing Co., Inc.
▪ Miller, R. A. (1994). Medical diagnostic decision support systems—past, present, and future.
Journal of the American Medical Informatics Association, 1(1), 8-27.
▪ Dinov, I. D. (2016). Volume and value of big healthcare data. Journal of medical statistics and
informatics, 4.
▪ Dougherty, G. (2009). Digital image processing for medical applications. Cambridge University
Press.
▪ Jasmine, D. (2014). Digital image processing for medical applications. Cambridge University Press.
▪ Koomey, J., Berard, S., Sanchez, M., & Wong, H. (2011). Implications of historical trends in the
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gclid=CjwKCAiAtdGNBhAmEiwAWxGcUgrfgzG_geudv4ty1qwq9AS3VEGujCB7sQ8SQH7S-
qFW97K5ufC1nRoCU64QAvD_BwE

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