Guilhem Julia is Lecturer at Sorbonne Paris Nord University, France, and Co-Vice-Dean of research at the Faculty of Law.Anne Fauchon is Senior Lecturer (HDR) at Sorbonne Paris Nord University, France, and Dean of the Faculty of Law, Political and Social Sciences.Rushed Kanawati is Lecturer and Researcher in Computer Science at Sorbonne Paris Nord University, France, specializing in Machine Learning and Complex Network Analysis.

• Preface ixAnne FAUCHONIntroduction xiCéline BLOUD-REYPart 1. Artificial Intelligence to Support Diagnosis 1Chapter 1. Healthcare Applications 3Anne CAMMILLERI1.1. The uses of a healthcare application 41.2. Applications at the service of hospitals (HR dimension) 61.2.1. Internal staff and patient management applications in public and private hospitals 61.2.2. Helpful applications to counter isolation and other vulnerabilities 101.3. Cybersecurity to reinforce the resilience of applications 131.3.1. The obligation to protect the health of minor children against content that may harm their health 141.3.2. Assessing health risks through digital services 161.4. Conclusion 211.5. References 21Chapter 2. Behavioral Insurance: The Latest Trick of Capitalism? 25Philippe BATIFOULIER and Nicolas DA SILVA2.1. Introduction 252.2. A new health insurance market 282.2.1. Healthy lifestyle behavior insurance: an overview 282.2.2. The importance of the institutional context 302.3. Heading towards a great backward leap in health risk socialization? 342.3.1. Artificial intelligence at the service of paternalism in insurance policies 342.3.2. Building on social inequalities 362.4. Conclusion 382.5. References 40Chapter 3. Artificial Intelligence and Health: Description of the Ecosystem Required for an Effective Use of AI 43Thomas LEFÈVRE3.1. General data ecosystem for data mining and algorithm development 433.1.1. An ecosystem for a digital "revolution" 433.1.2. Some preliminary socio-historical and technical elements 453.1.3. A digital approach from the inside of health… 453.1.4. …and an external approach to health 483.1.5. The evolution of regulatory aspects 493.2. From data to the algorithm at present: the role of research 503.2.1. What kind of research is there for health-related AI nowadays? 513.2.2. Beyond research, the integration of algorithms in the practitioner's environment 523.2.3. An intermediate case for integrating AI into the practitioner's environment: learning systems 543.2.4. Articulation between research and practice 543.3. Examining the quality of reporting of health-related AI research and the readiness of these AI forms through two medical examples 563.3.1. Screening or "predicting" post-traumatic stress disorder with AI 563.3.2. AI to assist the medical examiner in investigation 583.4. Integration and appropriation of algorithms 593.4.1. Technical integration of algorithms, microsocial integration: about appropriation 603.4.2. An example of algorithm integration and socio-technical evaluation: the Big Data Drop IT project 623.4.3. The contribution of mixed methods and the interdisciplinary approach for the end-to-end development of an algorithm - the I-ADViSe project 653.5. The integration of AI in a broader ecosystem than the practitioner's immediate environment: questions and perspectives 663.5.1. The illusions of immediacy and the dematerialized 673.5.2. An opposite problem: what place for health professionals in an ecosystem conducive to the use of AI? 683.5.3. AI as a technical object above all and technology as the common denominator for all our social activities 683.6. References 69Part 2. Artificial Intelligence at the Service of Healthcare 73Chapter 4. Legal Liability of Companion Robots 75Guilhem JULIA4.1. Introduction 754.2. Common law liability 844.2.1. The result of human activity 844.2.2. Caused by the thing 914.3. Special liability regimes 964.3.1. Caused by road accidents 964.3.2. Caused by a defective product 984.4. Conclusion 1054.5. References 106Chapter 5. From Computer-assisted Surgery to AI-guided Surgery 109Thomas GRÉGORY, Younès BENNANI and Charles DACHEUX5.1. Computer-assisted surgery 1095.2. Mixed reality, a computer-assisted surgery tool and a key element of AI-guided surgery 1095.3. The concept of AI-guided surgery 1135.4. Conclusion 1155.5. References 115Chapter 6. Detection of Anatomical Structures and Lesions in Hand Surgery Through the Use of Artificial Intelligence 119Léo DÉCHAUMET, Younès BENNANI, Joseph KARKAZAN, Nosseiba BEN SALEM, Abir BARBARA, Charles DACHEUX and Thomas GRÉGORY6.1. Introduction 1196.2. Context 1206.2.1. Object detection 1206.2.2. Contrastive self-supervised learning 1216.3. Problem 1: anatomical structure and lesion detection 1226.3.1. Overview of the problem 1236.3.2. Modular approach 1246.3.3. Separate approach 1266.3.4. Evaluation method 1286.3.5. Results and opening 1296.4. Problem 2: endoscopic carpal tunnel release 1326.4.1. Classic approach to object detection 1326.4.2. Self-supervised learning approach 1356.4.3. Results 1386.5. Conclusion 1396.6. Appendices 1396.7. References 145Chapter 7. Surgical Diagnosis Augmented by Artificial Intelligence 149Nosseiba BEN SALEM, Younès BENNANI, Joseph KARKAZAN, Léo DÉCHAUMET, Abir BARBARA, Charles DACHEUX and Thomas GRÉGORY7.1. Introduction 1507.2. Fundamental framework and state of the art 1517.3. Modular learning for classification 1537.3.1. Methodology 1537.3.2. Results 1577.4. Self-learning for data labeling and segmentation 1647.4.1. Methodology 1647.4.2. Results 1677.5. Conclusion 1707.6. References 170Conclusion 173Didier GUÉVELList of Authors 181Index 183