Abstract

Today, many applications of artificial intelligence (AI) are offered in medicine with applications more or less respectful of the care relationship and personal data of patients. There are also many different models, methods, and tools that claim to be artificial intelligence, sometimes qualified from "weak AI" to "strong AI."

Several frameworks have already been developed to assess the ethical risks of the applications of AI algorithms in many medical specialties which are themselves very numerous and that relies on different decision processes. Thus, the potential use cases of artificial intelligence in healthcare are very numerous and complex. It is necessary to take into account the types of health applications or devices, the actors involved with their respective roles: developers, suppliers and potential users: more or less specialized and experimented caregivers involved in the caregiver-patient relationship.

The degree of autonomy of the systems, data sources, artificial intelligence algorithms used and quality and software engineering criteria, legal benchmarks (GDPR, AI-Act).

We therefore propose a new indicator called INDICIA that evaluates the different benefits/risks of proposed medical systems using AI according to a method that results from experience with AI and different approaches to AI in health. In the first part, the proposed approach takes into account the ethics of AI (6 axes): (Technological, semantic, acceptability, complexity, population, actors) along with European legal regulations (GDPR and AI-Act) and quality technical specifications related to software engineering. The ethics of AI in health must necessarily be based on medical ethics: general references from the National Ethics Advisory Committee (Comité National Consultatif d’Ethique) and specific references to adapt it to each patient using a field ethics method, that we published in 2008. The method is based on the caregiver-patient relationship where privacy and consent that ensure the trust necessary for the practice of medicine are shared, while respecting medical confidentiality in a context where health data stored in the institutions is increasingly shared, disclosed or cyberhacked.

The INDICIA indicator we propose is developed using a time fuzzy vector space object model (FVSOM), whose component objects define the axes of our model in order to show the evolution of (benefits/risks) of AI tools. AI applications and devices in health according to criteria expressed by the attributes of these objects.

These guidelines originate from models conceived by scholars and experts in AI. INDICIA is showing the strengths and weaknesses of each category of AI models with some examples.The modularity of our approach offers the possibility to reuse the first 6 axes of the indicator with some legal and software engineering milestones, in order to adapt it and to further propose general AI ethics guidelines.

Short Biography

Joël Colloc is Emeritus Professor of Le Havre Normandy University. He earned his M.D. at the medical faculty of Lyon and a specialty degree of forensic medicine with a degree of clinical toxicology. He received a MSc. degree of IT from the Business School of Lyon (IAE) and a MSc. degree of computer sciences from the engineering school INSA of Lyon. He served as forensic physician at the Edouard Herriot Hospital in the neurological emergency department to cure drug addicted people, medical ethics and developed drug and addiction database. He went on to earn his Ph.D. in computer sciences at the INSA of Lyon.

As Hospital assistant at the laboratory of medical computer science and he taught IT at the medical faculty. He was elected as associate professor in computer sciences at IAE of Lyon and he earned his accreditation to supervise researches in sciences at the Lyon 1 University. He is a Le Havre Normandy University professor in computer sciences since 2003.

His main research topics concern e-health and particularly: fuzzy vectorial spaces (FVS), multi-agent clinical decision support systems (MADSS) and knowledge bases, Case Based Reasoning, ontologies, nervous system modeling and cognitive sciences and AI applications in medicine and human sciences.

His human sciences researches try to conciliate the ethics of using Big Data in epidemiological studies, autonomous systems and robots and keeping ethics use of AI in order to improve clinical decision in medicine while preserving the patient-caregiver relationship, the privacy and the freewill choice of the patients.

Abstract

The Difference of Convex functions (DC) programming and DC Algorithms (DCAs) are power tools of non-convex programming and non-differentiable global optimization. These theoretical and algorithmic tools, becoming now classic and increasingly popular, have been successfully applied by researchers and practitioners all the world over to model and solve their real-world problems in various fields. Indeed, they are known as powerful non-convex optimization tools due to their robustness and performance compared to existing methods, their rapidity and scalability, and the flexibility of DC decompositions. Researchers recognized that most of real-world problems can be formulated / reformulated as DC programs, and almost all classical and recent algorithms in nonconvex optimization can be seen as a version of DCA with an appropriate DC decomposition.

Curriculum Vitae

Prof. Le Thi Hoai An earned her PhD with Highest Distinction in Optimization in 1994. From 1998 to 2003 she was Associate Professor in Applied Mathematics at the National Institute for Applied Sciences, Rouen, and from 2003 to 2012 she was Full Professor in Computer Science at the University of Paul Verlaine – Metz. Since 2012 she has been Full Professor exceptional class, University of Lorraine. She is the holder of the Knight in the Order of Academic Palms Award of French government in July 2013. She obtained the Rosenbrock Prize 2017 of Springer awarded to the authors of the best paper published in the journal Optimization and Engineering., she was nominated a Senior Member of the Academic Institute of France (IUF) since June 2021, and received the 2021 Constantin Caratheodory Prize of the International Society of Global Optimization which rewards outstanding fundamental contributions that have stood the test of time to theory, algorithms, and applications of global optimization. She is the author/co-author of more than 300 journal articles, international conference papers and book chapters, the co-editor of 24 books and/or special issues of international journals, and supervisor of 40 PhD theses/Habilitation and is the leader of several great joint projects in Industry 4.0 framework with Big companies including RTE (French transmission system operator) and NAVAL group (the European leader in Naval defence and a major player in marine renewable energies). Prof. Le Thi Hoai An is the co-founder of DC programming and DCA with Professor Pham Dinh Tao.