Chapter 11 Implementation of AI in pathology
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Rishabha Malviya
Abstract
The increasing use of digital technologies has made it possible to incorporate machine learning and artificial intelligence (AI) into pathology. However, the utilization of these advancements is still in its earliest stages, and no randomized prospective trials have yet been conducted to illustrate the benefits of AI-based diagnostics. The present body of research pertaining to AI in the domain of pathology primarily centers around helping in routine diagnostic tasks and facilitating prognostication. The healthcare industry is just now beginning to acknowledge the potential of AI, a rapidly growing area of modern technology. The field of pathology is expected to have significant effects from the implementation of AI. The growing prevalence of digital pathology (DP) by laboratories is expected to serve as a crucial foundation for the implementation of these technologies, leading to their practical utilization in diagnostic practice. The utilization of AI in the domain of pathology holds promise for the development of image analysis tools. These tools have the ability to function as diagnostic aids or to provide new perspectives on disease biology, complementing the capabilities of human observers. DP has become a well-known concept among pathologists; nonetheless, comprehending the engineering and mathematical principles underlying DP remains challenging for a significant number of pathologists. Computer-aided pathology (CAP) facilitates the diagnostic process for pathologists. However, there are individuals who regard CAP as a potential threat to the pathology profession and have issues about its clinical efficacy. The introduction of DP poses significant challenges for pathologists due to the need to address technical considerations, workflow implications, and information technology infrastructure.
Abstract
The increasing use of digital technologies has made it possible to incorporate machine learning and artificial intelligence (AI) into pathology. However, the utilization of these advancements is still in its earliest stages, and no randomized prospective trials have yet been conducted to illustrate the benefits of AI-based diagnostics. The present body of research pertaining to AI in the domain of pathology primarily centers around helping in routine diagnostic tasks and facilitating prognostication. The healthcare industry is just now beginning to acknowledge the potential of AI, a rapidly growing area of modern technology. The field of pathology is expected to have significant effects from the implementation of AI. The growing prevalence of digital pathology (DP) by laboratories is expected to serve as a crucial foundation for the implementation of these technologies, leading to their practical utilization in diagnostic practice. The utilization of AI in the domain of pathology holds promise for the development of image analysis tools. These tools have the ability to function as diagnostic aids or to provide new perspectives on disease biology, complementing the capabilities of human observers. DP has become a well-known concept among pathologists; nonetheless, comprehending the engineering and mathematical principles underlying DP remains challenging for a significant number of pathologists. Computer-aided pathology (CAP) facilitates the diagnostic process for pathologists. However, there are individuals who regard CAP as a potential threat to the pathology profession and have issues about its clinical efficacy. The introduction of DP poses significant challenges for pathologists due to the need to address technical considerations, workflow implications, and information technology infrastructure.
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- Chapter 1 Cardiovascular disease diagnosis using AI-based imaging 1
- Chapter 2 Integration of AI in the management of bone health 23
- Chapter 3 AI for remote patient monitoring in healthcare 53
- Chapter 4 Engaging AI in emergency medicine for better patient care 91
- Chapter 5 Application of AI in ENT (otorhinolaryngology) care 109
- Chapter 6 Integration of AI in brain tumor surgery 125
- Chapter 7 AI in dentistry: role and application 155
- Chapter 8 Managing OPD with AI: implementation and utilization 181
- Chapter 9 Elder patient care and monitoring through AI 203
- Chapter 10 AI and pregnancy: an unexpected alliance 227
- Chapter 11 Implementation of AI in pathology 247
- Index 269
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- Chapter 1 Cardiovascular disease diagnosis using AI-based imaging 1
- Chapter 2 Integration of AI in the management of bone health 23
- Chapter 3 AI for remote patient monitoring in healthcare 53
- Chapter 4 Engaging AI in emergency medicine for better patient care 91
- Chapter 5 Application of AI in ENT (otorhinolaryngology) care 109
- Chapter 6 Integration of AI in brain tumor surgery 125
- Chapter 7 AI in dentistry: role and application 155
- Chapter 8 Managing OPD with AI: implementation and utilization 181
- Chapter 9 Elder patient care and monitoring through AI 203
- Chapter 10 AI and pregnancy: an unexpected alliance 227
- Chapter 11 Implementation of AI in pathology 247
- Index 269
