Details

Al, Healthcare and Law

ISTE Invoiced 1. Aufl.

von: Guilhem Julia, Anne Fauchon, Rushed Kanawati
142,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	03.07.2024
ISBN/EAN:	9781394306398
Sprache:	englisch
Anzahl Seiten:	224

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Beschreibungen

Titelbeschreibung

In a fully digitized world and hyper-connected society, artificial intelligence (AI) is developing more and more each day. In the aftermath of the Covid-19 pandemic, it seems appropriate to examine the real or imagined progress of AI in terms of human health. Like artificial intelligence, health is a field that involves a wide range of research disciplines. In order to better define and understand these social and technical developments, Al, Healthcare and Law brings together the thoughts and analyses of doctors, lawyers, economists and computer scientists. Through a wide range of original overviews of the issues involved, the book addresses questions such as the development of telemedicine, the use of medical data, the increased human perspective or medical ethics, and takes a multi-disciplinary and accessible approach to questioning the relationship between humans and computers, between the intimate and the machine.

Inhaltsverzeichnis

Preface ix Anne FAUCHON Introduction xi Céline BLOUD-REY Part 1. Artificial Intelligence to Support Diagnosis 1 Chapter 1. Healthcare Applications 3 Anne CAMMILLERI 1.1. The uses of a healthcare application 4 1.2. Applications at the service of hospitals (HR dimension) 6 1.2.1. Internal staff and patient management applications in public and private hospitals 6 1.2.2. Helpful applications to counter isolation and other vulnerabilities 10 1.3. Cybersecurity to reinforce the resilience of applications 13 1.3.1. The obligation to protect the health of minor children against content that may harm their health 14 1.3.2. Assessing health risks through digital services 16 1.4. Conclusion 21 1.5. References 21 Chapter 2. Behavioral Insurance: The Latest Trick of Capitalism? 25 Philippe BATIFOULIER and Nicolas DA SILVA 2.1. Introduction 25 2.2. A new health insurance market 28 2.2.1. Healthy lifestyle behavior insurance: an overview 28 2.2.2. The importance of the institutional context 30 2.3. Heading towards a great backward leap in health risk socialization? 34 2.3.1. Artificial intelligence at the service of paternalism in insurance policies 34 2.3.2. Building on social inequalities 36 2.4. Conclusion 38 2.5. References 40 Chapter 3. Artificial Intelligence and Health: Description of the Ecosystem Required for an Effective Use of AI 43 Thomas LEFÈVRE 3.1. General data ecosystem for data mining and algorithm development 43 3.1.1. An ecosystem for a digital "revolution" 43 3.1.2. Some preliminary socio-historical and technical elements 45 3.1.3. A digital approach from the inside of health… 45 3.1.4. …and an external approach to health 48 3.1.5. The evolution of regulatory aspects 49 3.2. From data to the algorithm at present: the role of research 50 3.2.1. What kind of research is there for health-related AI nowadays? 51 3.2.2. Beyond research, the integration of algorithms in the practitioner's environment 52 3.2.3. An intermediate case for integrating AI into the practitioner's environment: learning systems 54 3.2.4. Articulation between research and practice 54 3.3. Examining the quality of reporting of health-related AI research and the readiness of these AI forms through two medical examples 56 3.3.1. Screening or "predicting" post-traumatic stress disorder with AI 56 3.3.2. AI to assist the medical examiner in investigation 58 3.4. Integration and appropriation of algorithms 59 3.4.1. Technical integration of algorithms, microsocial integration: about appropriation 60 3.4.2. An example of algorithm integration and socio-technical evaluation: the Big Data Drop IT project 62 3.4.3. The contribution of mixed methods and the interdisciplinary approach for the end-to-end development of an algorithm - the I-ADViSe project 65 3.5. The integration of AI in a broader ecosystem than the practitioner's immediate environment: questions and perspectives 66 3.5.1. The illusions of immediacy and the dematerialized 67 3.5.2. An opposite problem: what place for health professionals in an ecosystem conducive to the use of AI? 68 3.5.3. AI as a technical object above all and technology as the common denominator for all our social activities 68 3.6. References 69 Part 2. Artificial Intelligence at the Service of Healthcare 73 Chapter 4. Legal Liability of Companion Robots 75 Guilhem JULIA 4.1. Introduction 75 4.2. Common law liability 84 4.2.1. The result of human activity 84 4.2.2. Caused by the thing 91 4.3. Special liability regimes 96 4.3.1. Caused by road accidents 96 4.3.2. Caused by a defective product 98 4.4. Conclusion 105 4.5. References 106 Chapter 5. From Computer-assisted Surgery to AI-guided Surgery 109 Thomas GRÉGORY, Younès BENNANI and Charles DACHEUX 5.1. Computer-assisted surgery 109 5.2. Mixed reality, a computer-assisted surgery tool and a key element of AI-guided surgery 109 5.3. The concept of AI-guided surgery 113 5.4. Conclusion 115 5.5. References 115 Chapter 6. Detection of Anatomical Structures and Lesions in Hand Surgery Through the Use of Artificial Intelligence 119 Léo DÉCHAUMET, Younès BENNANI, Joseph KARKAZAN, Nosseiba BEN SALEM, Abir BARBARA, Charles DACHEUX and Thomas GRÉGORY 6.1. Introduction 119 6.2. Context 120 6.2.1. Object detection 120 6.2.2. Contrastive self-supervised learning 121 6.3. Problem 1: anatomical structure and lesion detection 122 6.3.1. Overview of the problem 123 6.3.2. Modular approach 124 6.3.3. Separate approach 126 6.3.4. Evaluation method 128 6.3.5. Results and opening 129 6.4. Problem 2: endoscopic carpal tunnel release 132 6.4.1. Classic approach to object detection 132 6.4.2. Self-supervised learning approach 135 6.4.3. Results 138 6.5. Conclusion 139 6.6. Appendices 139 6.7. References 145 Chapter 7. Surgical Diagnosis Augmented by Artificial Intelligence 149 Nosseiba BEN SALEM, Younès BENNANI, Joseph KARKAZAN, Léo DÉCHAUMET, Abir BARBARA, Charles DACHEUX and Thomas GRÉGORY 7.1. Introduction 150 7.2. Fundamental framework and state of the art 151 7.3. Modular learning for classification 153 7.3.1. Methodology 153 7.3.2. Results 157 7.4. Self-learning for data labeling and segmentation 164 7.4.1. Methodology 164 7.4.2. Results 167 7.5. Conclusion 170 7.6. References 170 Conclusion 173 Didier GUÉVEL List of Authors 181 Index 183

Autorenportrait

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.