Artificial Intelligence in Health
Herausgeber: Sarazin, Marianne
Artificial Intelligence in Health
Herausgeber: Sarazin, Marianne
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Undeniable, inescapable, exhilarating and breaking free from the exclusive domain of science, artificial intelligence has become our main preoccupation. A major generator of new mathematical thinking, AI is the result of easy access to information and data, as facilitated by computer technology. Big Data has come to be seen as an unlimited source of knowledge, the use of which is still being fully explored, but its industrialization has swiftly followed in the footsteps of mathematicians; today's tools are increasingly designed to replace human beings, which comes with social and philosophical…mehr
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Undeniable, inescapable, exhilarating and breaking free from the exclusive domain of science, artificial intelligence has become our main preoccupation. A major generator of new mathematical thinking, AI is the result of easy access to information and data, as facilitated by computer technology. Big Data has come to be seen as an unlimited source of knowledge, the use of which is still being fully explored, but its industrialization has swiftly followed in the footsteps of mathematicians; today's tools are increasingly designed to replace human beings, which comes with social and philosophical consequences. Drawing on examples of scientific work and the insights of experts, this book offers food for thought on the consequences and future of AI technology in education, health, the workplace and aging.
Produktdetails
- Produktdetails
- Verlag: Wiley
- Seitenzahl: 240
- Erscheinungstermin: 26. März 2024
- Englisch
- Abmessung: 234mm x 156mm x 14mm
- Gewicht: 494g
- ISBN-13: 9781786308894
- ISBN-10: 1786308894
- Artikelnr.: 69919111
- Verlag: Wiley
- Seitenzahl: 240
- Erscheinungstermin: 26. März 2024
- Englisch
- Abmessung: 234mm x 156mm x 14mm
- Gewicht: 494g
- ISBN-13: 9781786308894
- ISBN-10: 1786308894
- Artikelnr.: 69919111
Marianne Sarazin is a public health doctor with a doctorate in Life Sciences from the Engineering and Health Center (CIS) of the École nationale supérieure des mines de Saint-Étienne, France. She is the Head of the Medical Information department of the Mutualiste Sanitary Group of Saint-Étienne (Aesio group) and a CIS collaborator in the Optimization of Healthcare Systems department as well as UMRS 1136 Inserm, specializing in the modeling of epidemics.
Author Presentation xi Preface xv Marianne SARAZIN Part 1 Growing with
Artificial Intelligence 1 Introduction to Part 1 3 Marianne SARAZIN Chapter
1 From Human to Artificial Intelligence 5 Bruno SALGUES 1.1 The different
forms of intelligence 5 1.1.1 Human intelligence typologies 5 1.1.2
Artificial intelligence (AI) and human intelligence 5 1.1.3 Object
intelligence and human assistance 5 1.2 History of "artificial"
intelligence 7 1.2.1 Mechanical forms 7 1.2.2 The desire to model neurons
and cybernetics 8 1.2.3 The arrival of computers 9 1.2.4 Different uses of
artificial intelligence in healthcare 12 1.2.5 Automated fields or scored
answers 15 1.2.6 Expert systems 16 1.2.7 Vector differentiation or vector
forest 17 1.2.8 Convolution matrix 18 1.2.9 Multi-cameral or democratic
systems 19 1.2.10 System dynamics 19 1.2.11 Machine learning 20 1.2.12 Deep
learning 22 1.2.13 The keys to adopting artificial intelligence in
healthcare 23 1.2.14 Organ processing using artificial intelligence 24
1.2.15 Medical procedures aided by artificial intelligence: drug dosage 25
Chapter 2 The Philosopher's Point of View: The Challenges of AI for Our
Humanity 29 François-Xavier CLÉMENT 2.1 Introduction 29 2.2 The beginnings
of AI 30 2.3 Man: master or slave of AI - examples of behavioral approaches
33 2.3.1 Teenagers and their phones 33 2.3.2 Consumer behavior and AI 34
2.3.3 Memory use and AI 34 2.3.4 Human intelligence versus AI 35 2.3.5 Game
master: human or AI? 37 2.4 And what about humanity? 38 2.4.1 New language
38 2.4.2 New thinking 40 2.4.3 A new moral 41 2.5 AI in education: changing
learning styles among students in 2020 43 2.5.1 Generations and technology
43 2.5.2 Student behavior 44 2.6 A few concluding words 45 Part 2 Working
with Artificial Intelligence 47 Introduction to Part 2 49 Marianne SARAZIN
Chapter 3 For Strategic and Responsible "Piloting" of AI-related Open
Innovation Projects 51 Aline COURIE-LEMEUR 3.1 Introduction 51 3.2
Innovation development and the "open innovation" model: challenges and
risks 52 3.2.1 Definition of open innovation 52 3.2.2 Dangers of open
innovation 54 3.3 Steering "open innovation" projects as part of the
development of artificial intelligence techniques 56 3.3.1 Strategic and
responsible management 57 3.3.2 The attributes of strategic, responsible
management 60 3.4 In a nutshell 63 3.5 Conclusion 66 Chapter 4 Management
and AI: Myths and Realities 67 Gilles ROUET 4.1 Introduction 67 4.1.1
Paradigm shift in the business world 67 4.1.2 New management model 68 4.1.3
Mixing genres: intrusion of everyday technologies into the business world
68 4.2 Management in our digital environment 69 4.2.1 The contribution of
digital technology to companies 70 4.2.2 Artificial intelligence 72 4.2.3
Analysis concepts for large databases 73 4.3 Humans and machines 76 4.3.1
Optimizing AI-based tools within companies 77 4.3.2 Putting people before
AI in companies 78 4.3.3 Evolution of essential skills 79 4.3.4 And then...
80 4.4 Conclusion 81 Part 3 Managing Healthcare with Artificial
Intelligence 83 Introduction to Part 3 85 Marianne SARAZIN Chapter 5 How to
Bring the Medical World Out of the Pre-digital Age? 87 Marc SOLER 5.1
Introduction 87 5.2 Healthcare professionals' relationship with digital
technology 88 5.2.1 Level of training of healthcare professionals 88 5.2.2
Technology and healthcare professionals 90 5.3 Creating a universal medical
record: a utopia? 93 5.4 "Artificial intelligence" at the service of
healthcare: the French government's position 95 5.5 The approach of
technology suppliers? 96 5.6 IBM's Watson system: its history and
application to the medical field 98 5.6.1 Concept 98 5.6.2 Oncology
applications 99 5.6.3 An admission of failure 99 5.6.4 The clinician's
point of view 100 5.6.5 Illustrative examples 102 5.6.6 The importance of
source data 104 5.6.7 Conclusion 106 5.7 The role of start-ups 107 5.7.1 A
few examples 108 5.7.2 Special case of BenevolentAI 109 5.8 Conclusion 111
Chapter 6 Data Quality: A Major Challenge for AI in Healthcare 113 Marysa
GERMAIN 6.1 Introduction 113 6.2 From patient data to AI 114 6.2.1 The
legal framework for processing medical data 114 6.2.2 The technological
framework 117 6.2.3 The hospital's database management department: the DIM
118 6.2.4 Caregivers' views on the digitization of medical information 120
6.3 Data quality and consolidation 120 6.3.1 Medical data 120 6.3.2
Implementation of a continuous process of quality improvement 122 Part 4
Aging with Artificial Intelligence 133 Introduction to Part 4 135 Marianne
SARAZIN Chapter 7 Proposed Method for Developing an Aging Score 137
Marianne SARAZIN 7.1 Introduction 137 7.2 Focus on the determinants of
age-related frailty 138 7.3 Choice of marker variables to determine age 139
7.3.1 Rational marker selection based on expertise: an operational approach
based on a literature review 139 7.3.2 Selection of markers using variables
with values within normality limits 141 7.3.3 Conclusion 144 7.4 Choice of
normal aging control population 145 7.4.1 Initial hypotheses defining the
choice of the control population and the construction of the score 145
7.4.2 First approach: rational selection of the control population based on
the literature 146 7.4.3 Second approach: selection of the control
population by classification using the dynamic clustering method 146 7.4.4
Results 148 7.4.5 Conclusion 150 7.5 Mathematical modeling of the aging
score 151 7.5.1 Initial concept 151 7.5.2 Calculating biological age from a
control population sample 152 7.5.3 Conclusions 159 7.6 Calculating
biological age: modeling dependence between marker variables using a
Gaussian copula 160 7.6.1 Method 160 7.6.2 Source population 162 7.6.3
Results 162 7.6.4 Conclusions 166 7.7 Calculation of biological age for any
population (using a Gaussian copula) 166 7.7.1 Method 166 7.7.2 Source
population 169 7.7.3 Results 169 7.7.4 Conclusions 175 7.8 Perspectives on
this work 176 7.8.1 Advantages and limitations of this work 176 7.8.2
Perspectives 178 Chapter 8 Automatic Detection of Behavioral Changes in a
Smart Home 179 Cyriak AZEFAC 8.1 Introduction 179 8.2 Definitions 181 8.3
Methodology 182 8.3.1 Attribute extraction 184 8.3.2 Unsupervised
classification 186 8.3.3 Auto-encoder 187 8.3.4 Clustering 188 8.4 Case
study: ARUBA 188 8.5 Conclusion 189 Conclusion 191 Marianne SARAZIN
References 193 List of Authors 203 Index 205
Artificial Intelligence 1 Introduction to Part 1 3 Marianne SARAZIN Chapter
1 From Human to Artificial Intelligence 5 Bruno SALGUES 1.1 The different
forms of intelligence 5 1.1.1 Human intelligence typologies 5 1.1.2
Artificial intelligence (AI) and human intelligence 5 1.1.3 Object
intelligence and human assistance 5 1.2 History of "artificial"
intelligence 7 1.2.1 Mechanical forms 7 1.2.2 The desire to model neurons
and cybernetics 8 1.2.3 The arrival of computers 9 1.2.4 Different uses of
artificial intelligence in healthcare 12 1.2.5 Automated fields or scored
answers 15 1.2.6 Expert systems 16 1.2.7 Vector differentiation or vector
forest 17 1.2.8 Convolution matrix 18 1.2.9 Multi-cameral or democratic
systems 19 1.2.10 System dynamics 19 1.2.11 Machine learning 20 1.2.12 Deep
learning 22 1.2.13 The keys to adopting artificial intelligence in
healthcare 23 1.2.14 Organ processing using artificial intelligence 24
1.2.15 Medical procedures aided by artificial intelligence: drug dosage 25
Chapter 2 The Philosopher's Point of View: The Challenges of AI for Our
Humanity 29 François-Xavier CLÉMENT 2.1 Introduction 29 2.2 The beginnings
of AI 30 2.3 Man: master or slave of AI - examples of behavioral approaches
33 2.3.1 Teenagers and their phones 33 2.3.2 Consumer behavior and AI 34
2.3.3 Memory use and AI 34 2.3.4 Human intelligence versus AI 35 2.3.5 Game
master: human or AI? 37 2.4 And what about humanity? 38 2.4.1 New language
38 2.4.2 New thinking 40 2.4.3 A new moral 41 2.5 AI in education: changing
learning styles among students in 2020 43 2.5.1 Generations and technology
43 2.5.2 Student behavior 44 2.6 A few concluding words 45 Part 2 Working
with Artificial Intelligence 47 Introduction to Part 2 49 Marianne SARAZIN
Chapter 3 For Strategic and Responsible "Piloting" of AI-related Open
Innovation Projects 51 Aline COURIE-LEMEUR 3.1 Introduction 51 3.2
Innovation development and the "open innovation" model: challenges and
risks 52 3.2.1 Definition of open innovation 52 3.2.2 Dangers of open
innovation 54 3.3 Steering "open innovation" projects as part of the
development of artificial intelligence techniques 56 3.3.1 Strategic and
responsible management 57 3.3.2 The attributes of strategic, responsible
management 60 3.4 In a nutshell 63 3.5 Conclusion 66 Chapter 4 Management
and AI: Myths and Realities 67 Gilles ROUET 4.1 Introduction 67 4.1.1
Paradigm shift in the business world 67 4.1.2 New management model 68 4.1.3
Mixing genres: intrusion of everyday technologies into the business world
68 4.2 Management in our digital environment 69 4.2.1 The contribution of
digital technology to companies 70 4.2.2 Artificial intelligence 72 4.2.3
Analysis concepts for large databases 73 4.3 Humans and machines 76 4.3.1
Optimizing AI-based tools within companies 77 4.3.2 Putting people before
AI in companies 78 4.3.3 Evolution of essential skills 79 4.3.4 And then...
80 4.4 Conclusion 81 Part 3 Managing Healthcare with Artificial
Intelligence 83 Introduction to Part 3 85 Marianne SARAZIN Chapter 5 How to
Bring the Medical World Out of the Pre-digital Age? 87 Marc SOLER 5.1
Introduction 87 5.2 Healthcare professionals' relationship with digital
technology 88 5.2.1 Level of training of healthcare professionals 88 5.2.2
Technology and healthcare professionals 90 5.3 Creating a universal medical
record: a utopia? 93 5.4 "Artificial intelligence" at the service of
healthcare: the French government's position 95 5.5 The approach of
technology suppliers? 96 5.6 IBM's Watson system: its history and
application to the medical field 98 5.6.1 Concept 98 5.6.2 Oncology
applications 99 5.6.3 An admission of failure 99 5.6.4 The clinician's
point of view 100 5.6.5 Illustrative examples 102 5.6.6 The importance of
source data 104 5.6.7 Conclusion 106 5.7 The role of start-ups 107 5.7.1 A
few examples 108 5.7.2 Special case of BenevolentAI 109 5.8 Conclusion 111
Chapter 6 Data Quality: A Major Challenge for AI in Healthcare 113 Marysa
GERMAIN 6.1 Introduction 113 6.2 From patient data to AI 114 6.2.1 The
legal framework for processing medical data 114 6.2.2 The technological
framework 117 6.2.3 The hospital's database management department: the DIM
118 6.2.4 Caregivers' views on the digitization of medical information 120
6.3 Data quality and consolidation 120 6.3.1 Medical data 120 6.3.2
Implementation of a continuous process of quality improvement 122 Part 4
Aging with Artificial Intelligence 133 Introduction to Part 4 135 Marianne
SARAZIN Chapter 7 Proposed Method for Developing an Aging Score 137
Marianne SARAZIN 7.1 Introduction 137 7.2 Focus on the determinants of
age-related frailty 138 7.3 Choice of marker variables to determine age 139
7.3.1 Rational marker selection based on expertise: an operational approach
based on a literature review 139 7.3.2 Selection of markers using variables
with values within normality limits 141 7.3.3 Conclusion 144 7.4 Choice of
normal aging control population 145 7.4.1 Initial hypotheses defining the
choice of the control population and the construction of the score 145
7.4.2 First approach: rational selection of the control population based on
the literature 146 7.4.3 Second approach: selection of the control
population by classification using the dynamic clustering method 146 7.4.4
Results 148 7.4.5 Conclusion 150 7.5 Mathematical modeling of the aging
score 151 7.5.1 Initial concept 151 7.5.2 Calculating biological age from a
control population sample 152 7.5.3 Conclusions 159 7.6 Calculating
biological age: modeling dependence between marker variables using a
Gaussian copula 160 7.6.1 Method 160 7.6.2 Source population 162 7.6.3
Results 162 7.6.4 Conclusions 166 7.7 Calculation of biological age for any
population (using a Gaussian copula) 166 7.7.1 Method 166 7.7.2 Source
population 169 7.7.3 Results 169 7.7.4 Conclusions 175 7.8 Perspectives on
this work 176 7.8.1 Advantages and limitations of this work 176 7.8.2
Perspectives 178 Chapter 8 Automatic Detection of Behavioral Changes in a
Smart Home 179 Cyriak AZEFAC 8.1 Introduction 179 8.2 Definitions 181 8.3
Methodology 182 8.3.1 Attribute extraction 184 8.3.2 Unsupervised
classification 186 8.3.3 Auto-encoder 187 8.3.4 Clustering 188 8.4 Case
study: ARUBA 188 8.5 Conclusion 189 Conclusion 191 Marianne SARAZIN
References 193 List of Authors 203 Index 205
Author Presentation xi Preface xv Marianne SARAZIN Part 1 Growing with
Artificial Intelligence 1 Introduction to Part 1 3 Marianne SARAZIN Chapter
1 From Human to Artificial Intelligence 5 Bruno SALGUES 1.1 The different
forms of intelligence 5 1.1.1 Human intelligence typologies 5 1.1.2
Artificial intelligence (AI) and human intelligence 5 1.1.3 Object
intelligence and human assistance 5 1.2 History of "artificial"
intelligence 7 1.2.1 Mechanical forms 7 1.2.2 The desire to model neurons
and cybernetics 8 1.2.3 The arrival of computers 9 1.2.4 Different uses of
artificial intelligence in healthcare 12 1.2.5 Automated fields or scored
answers 15 1.2.6 Expert systems 16 1.2.7 Vector differentiation or vector
forest 17 1.2.8 Convolution matrix 18 1.2.9 Multi-cameral or democratic
systems 19 1.2.10 System dynamics 19 1.2.11 Machine learning 20 1.2.12 Deep
learning 22 1.2.13 The keys to adopting artificial intelligence in
healthcare 23 1.2.14 Organ processing using artificial intelligence 24
1.2.15 Medical procedures aided by artificial intelligence: drug dosage 25
Chapter 2 The Philosopher's Point of View: The Challenges of AI for Our
Humanity 29 François-Xavier CLÉMENT 2.1 Introduction 29 2.2 The beginnings
of AI 30 2.3 Man: master or slave of AI - examples of behavioral approaches
33 2.3.1 Teenagers and their phones 33 2.3.2 Consumer behavior and AI 34
2.3.3 Memory use and AI 34 2.3.4 Human intelligence versus AI 35 2.3.5 Game
master: human or AI? 37 2.4 And what about humanity? 38 2.4.1 New language
38 2.4.2 New thinking 40 2.4.3 A new moral 41 2.5 AI in education: changing
learning styles among students in 2020 43 2.5.1 Generations and technology
43 2.5.2 Student behavior 44 2.6 A few concluding words 45 Part 2 Working
with Artificial Intelligence 47 Introduction to Part 2 49 Marianne SARAZIN
Chapter 3 For Strategic and Responsible "Piloting" of AI-related Open
Innovation Projects 51 Aline COURIE-LEMEUR 3.1 Introduction 51 3.2
Innovation development and the "open innovation" model: challenges and
risks 52 3.2.1 Definition of open innovation 52 3.2.2 Dangers of open
innovation 54 3.3 Steering "open innovation" projects as part of the
development of artificial intelligence techniques 56 3.3.1 Strategic and
responsible management 57 3.3.2 The attributes of strategic, responsible
management 60 3.4 In a nutshell 63 3.5 Conclusion 66 Chapter 4 Management
and AI: Myths and Realities 67 Gilles ROUET 4.1 Introduction 67 4.1.1
Paradigm shift in the business world 67 4.1.2 New management model 68 4.1.3
Mixing genres: intrusion of everyday technologies into the business world
68 4.2 Management in our digital environment 69 4.2.1 The contribution of
digital technology to companies 70 4.2.2 Artificial intelligence 72 4.2.3
Analysis concepts for large databases 73 4.3 Humans and machines 76 4.3.1
Optimizing AI-based tools within companies 77 4.3.2 Putting people before
AI in companies 78 4.3.3 Evolution of essential skills 79 4.3.4 And then...
80 4.4 Conclusion 81 Part 3 Managing Healthcare with Artificial
Intelligence 83 Introduction to Part 3 85 Marianne SARAZIN Chapter 5 How to
Bring the Medical World Out of the Pre-digital Age? 87 Marc SOLER 5.1
Introduction 87 5.2 Healthcare professionals' relationship with digital
technology 88 5.2.1 Level of training of healthcare professionals 88 5.2.2
Technology and healthcare professionals 90 5.3 Creating a universal medical
record: a utopia? 93 5.4 "Artificial intelligence" at the service of
healthcare: the French government's position 95 5.5 The approach of
technology suppliers? 96 5.6 IBM's Watson system: its history and
application to the medical field 98 5.6.1 Concept 98 5.6.2 Oncology
applications 99 5.6.3 An admission of failure 99 5.6.4 The clinician's
point of view 100 5.6.5 Illustrative examples 102 5.6.6 The importance of
source data 104 5.6.7 Conclusion 106 5.7 The role of start-ups 107 5.7.1 A
few examples 108 5.7.2 Special case of BenevolentAI 109 5.8 Conclusion 111
Chapter 6 Data Quality: A Major Challenge for AI in Healthcare 113 Marysa
GERMAIN 6.1 Introduction 113 6.2 From patient data to AI 114 6.2.1 The
legal framework for processing medical data 114 6.2.2 The technological
framework 117 6.2.3 The hospital's database management department: the DIM
118 6.2.4 Caregivers' views on the digitization of medical information 120
6.3 Data quality and consolidation 120 6.3.1 Medical data 120 6.3.2
Implementation of a continuous process of quality improvement 122 Part 4
Aging with Artificial Intelligence 133 Introduction to Part 4 135 Marianne
SARAZIN Chapter 7 Proposed Method for Developing an Aging Score 137
Marianne SARAZIN 7.1 Introduction 137 7.2 Focus on the determinants of
age-related frailty 138 7.3 Choice of marker variables to determine age 139
7.3.1 Rational marker selection based on expertise: an operational approach
based on a literature review 139 7.3.2 Selection of markers using variables
with values within normality limits 141 7.3.3 Conclusion 144 7.4 Choice of
normal aging control population 145 7.4.1 Initial hypotheses defining the
choice of the control population and the construction of the score 145
7.4.2 First approach: rational selection of the control population based on
the literature 146 7.4.3 Second approach: selection of the control
population by classification using the dynamic clustering method 146 7.4.4
Results 148 7.4.5 Conclusion 150 7.5 Mathematical modeling of the aging
score 151 7.5.1 Initial concept 151 7.5.2 Calculating biological age from a
control population sample 152 7.5.3 Conclusions 159 7.6 Calculating
biological age: modeling dependence between marker variables using a
Gaussian copula 160 7.6.1 Method 160 7.6.2 Source population 162 7.6.3
Results 162 7.6.4 Conclusions 166 7.7 Calculation of biological age for any
population (using a Gaussian copula) 166 7.7.1 Method 166 7.7.2 Source
population 169 7.7.3 Results 169 7.7.4 Conclusions 175 7.8 Perspectives on
this work 176 7.8.1 Advantages and limitations of this work 176 7.8.2
Perspectives 178 Chapter 8 Automatic Detection of Behavioral Changes in a
Smart Home 179 Cyriak AZEFAC 8.1 Introduction 179 8.2 Definitions 181 8.3
Methodology 182 8.3.1 Attribute extraction 184 8.3.2 Unsupervised
classification 186 8.3.3 Auto-encoder 187 8.3.4 Clustering 188 8.4 Case
study: ARUBA 188 8.5 Conclusion 189 Conclusion 191 Marianne SARAZIN
References 193 List of Authors 203 Index 205
Artificial Intelligence 1 Introduction to Part 1 3 Marianne SARAZIN Chapter
1 From Human to Artificial Intelligence 5 Bruno SALGUES 1.1 The different
forms of intelligence 5 1.1.1 Human intelligence typologies 5 1.1.2
Artificial intelligence (AI) and human intelligence 5 1.1.3 Object
intelligence and human assistance 5 1.2 History of "artificial"
intelligence 7 1.2.1 Mechanical forms 7 1.2.2 The desire to model neurons
and cybernetics 8 1.2.3 The arrival of computers 9 1.2.4 Different uses of
artificial intelligence in healthcare 12 1.2.5 Automated fields or scored
answers 15 1.2.6 Expert systems 16 1.2.7 Vector differentiation or vector
forest 17 1.2.8 Convolution matrix 18 1.2.9 Multi-cameral or democratic
systems 19 1.2.10 System dynamics 19 1.2.11 Machine learning 20 1.2.12 Deep
learning 22 1.2.13 The keys to adopting artificial intelligence in
healthcare 23 1.2.14 Organ processing using artificial intelligence 24
1.2.15 Medical procedures aided by artificial intelligence: drug dosage 25
Chapter 2 The Philosopher's Point of View: The Challenges of AI for Our
Humanity 29 François-Xavier CLÉMENT 2.1 Introduction 29 2.2 The beginnings
of AI 30 2.3 Man: master or slave of AI - examples of behavioral approaches
33 2.3.1 Teenagers and their phones 33 2.3.2 Consumer behavior and AI 34
2.3.3 Memory use and AI 34 2.3.4 Human intelligence versus AI 35 2.3.5 Game
master: human or AI? 37 2.4 And what about humanity? 38 2.4.1 New language
38 2.4.2 New thinking 40 2.4.3 A new moral 41 2.5 AI in education: changing
learning styles among students in 2020 43 2.5.1 Generations and technology
43 2.5.2 Student behavior 44 2.6 A few concluding words 45 Part 2 Working
with Artificial Intelligence 47 Introduction to Part 2 49 Marianne SARAZIN
Chapter 3 For Strategic and Responsible "Piloting" of AI-related Open
Innovation Projects 51 Aline COURIE-LEMEUR 3.1 Introduction 51 3.2
Innovation development and the "open innovation" model: challenges and
risks 52 3.2.1 Definition of open innovation 52 3.2.2 Dangers of open
innovation 54 3.3 Steering "open innovation" projects as part of the
development of artificial intelligence techniques 56 3.3.1 Strategic and
responsible management 57 3.3.2 The attributes of strategic, responsible
management 60 3.4 In a nutshell 63 3.5 Conclusion 66 Chapter 4 Management
and AI: Myths and Realities 67 Gilles ROUET 4.1 Introduction 67 4.1.1
Paradigm shift in the business world 67 4.1.2 New management model 68 4.1.3
Mixing genres: intrusion of everyday technologies into the business world
68 4.2 Management in our digital environment 69 4.2.1 The contribution of
digital technology to companies 70 4.2.2 Artificial intelligence 72 4.2.3
Analysis concepts for large databases 73 4.3 Humans and machines 76 4.3.1
Optimizing AI-based tools within companies 77 4.3.2 Putting people before
AI in companies 78 4.3.3 Evolution of essential skills 79 4.3.4 And then...
80 4.4 Conclusion 81 Part 3 Managing Healthcare with Artificial
Intelligence 83 Introduction to Part 3 85 Marianne SARAZIN Chapter 5 How to
Bring the Medical World Out of the Pre-digital Age? 87 Marc SOLER 5.1
Introduction 87 5.2 Healthcare professionals' relationship with digital
technology 88 5.2.1 Level of training of healthcare professionals 88 5.2.2
Technology and healthcare professionals 90 5.3 Creating a universal medical
record: a utopia? 93 5.4 "Artificial intelligence" at the service of
healthcare: the French government's position 95 5.5 The approach of
technology suppliers? 96 5.6 IBM's Watson system: its history and
application to the medical field 98 5.6.1 Concept 98 5.6.2 Oncology
applications 99 5.6.3 An admission of failure 99 5.6.4 The clinician's
point of view 100 5.6.5 Illustrative examples 102 5.6.6 The importance of
source data 104 5.6.7 Conclusion 106 5.7 The role of start-ups 107 5.7.1 A
few examples 108 5.7.2 Special case of BenevolentAI 109 5.8 Conclusion 111
Chapter 6 Data Quality: A Major Challenge for AI in Healthcare 113 Marysa
GERMAIN 6.1 Introduction 113 6.2 From patient data to AI 114 6.2.1 The
legal framework for processing medical data 114 6.2.2 The technological
framework 117 6.2.3 The hospital's database management department: the DIM
118 6.2.4 Caregivers' views on the digitization of medical information 120
6.3 Data quality and consolidation 120 6.3.1 Medical data 120 6.3.2
Implementation of a continuous process of quality improvement 122 Part 4
Aging with Artificial Intelligence 133 Introduction to Part 4 135 Marianne
SARAZIN Chapter 7 Proposed Method for Developing an Aging Score 137
Marianne SARAZIN 7.1 Introduction 137 7.2 Focus on the determinants of
age-related frailty 138 7.3 Choice of marker variables to determine age 139
7.3.1 Rational marker selection based on expertise: an operational approach
based on a literature review 139 7.3.2 Selection of markers using variables
with values within normality limits 141 7.3.3 Conclusion 144 7.4 Choice of
normal aging control population 145 7.4.1 Initial hypotheses defining the
choice of the control population and the construction of the score 145
7.4.2 First approach: rational selection of the control population based on
the literature 146 7.4.3 Second approach: selection of the control
population by classification using the dynamic clustering method 146 7.4.4
Results 148 7.4.5 Conclusion 150 7.5 Mathematical modeling of the aging
score 151 7.5.1 Initial concept 151 7.5.2 Calculating biological age from a
control population sample 152 7.5.3 Conclusions 159 7.6 Calculating
biological age: modeling dependence between marker variables using a
Gaussian copula 160 7.6.1 Method 160 7.6.2 Source population 162 7.6.3
Results 162 7.6.4 Conclusions 166 7.7 Calculation of biological age for any
population (using a Gaussian copula) 166 7.7.1 Method 166 7.7.2 Source
population 169 7.7.3 Results 169 7.7.4 Conclusions 175 7.8 Perspectives on
this work 176 7.8.1 Advantages and limitations of this work 176 7.8.2
Perspectives 178 Chapter 8 Automatic Detection of Behavioral Changes in a
Smart Home 179 Cyriak AZEFAC 8.1 Introduction 179 8.2 Definitions 181 8.3
Methodology 182 8.3.1 Attribute extraction 184 8.3.2 Unsupervised
classification 186 8.3.3 Auto-encoder 187 8.3.4 Clustering 188 8.4 Case
study: ARUBA 188 8.5 Conclusion 189 Conclusion 191 Marianne SARAZIN
References 193 List of Authors 203 Index 205