Chapter 4 AI for enhanced cancer detection and diagnosis
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Abstract
In the last 10 years, artificial intelligence (AI) has made impressive progress in imitating human cognitive abilities using complex mathematical algorithms, especially in the field of oncology. The intricate genetic and epigenetic variations of cancer pose significant difficulties in both diagnosing and treating the disease. The World Health Organization places a high importance on early cancer identification in order to enhance the chances of survival. However, there are ongoing difficulties in precisely identifying individuals and evaluating their level of risk. AI algorithms demonstrate substantial potential in addressing these challenges by identifying genetic anomalies and aberrant protein interactions at an early stage, while also improving the precision of disease risk prediction, diagnosis, prognosis, and treatment planning. The incorporation of AI and machine learning in cancer care has the capacity to transform healthcare by facilitating individualized treatment methods and fostering immediate communication among researchers via digital platforms. As AI becomes more integrated into clinical practice, it is crucial to ensure its safe and ethical use in order to fully harness its promise. This progress not only clears the path for more efficient cancer diagnosis and treatment but also shows potential for enhancing overall patient care, establishing AI-driven innovations as a crucial element of future healthcare.
Abstract
In the last 10 years, artificial intelligence (AI) has made impressive progress in imitating human cognitive abilities using complex mathematical algorithms, especially in the field of oncology. The intricate genetic and epigenetic variations of cancer pose significant difficulties in both diagnosing and treating the disease. The World Health Organization places a high importance on early cancer identification in order to enhance the chances of survival. However, there are ongoing difficulties in precisely identifying individuals and evaluating their level of risk. AI algorithms demonstrate substantial potential in addressing these challenges by identifying genetic anomalies and aberrant protein interactions at an early stage, while also improving the precision of disease risk prediction, diagnosis, prognosis, and treatment planning. The incorporation of AI and machine learning in cancer care has the capacity to transform healthcare by facilitating individualized treatment methods and fostering immediate communication among researchers via digital platforms. As AI becomes more integrated into clinical practice, it is crucial to ensure its safe and ethical use in order to fully harness its promise. This progress not only clears the path for more efficient cancer diagnosis and treatment but also shows potential for enhancing overall patient care, establishing AI-driven innovations as a crucial element of future healthcare.
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- About the authors XVII
- Chapter 1 Artificial intelligence in cancer treatment and management 1
- Chapter 2 AI-based approaches to cancer drug discovery 27
- Chapter 3 Integrating AI and digital twin technology in cancer therapy 47
- Chapter 4 AI for enhanced cancer detection and diagnosis 63
- Chapter 5 AI-guided surgical interventions for cancer and tumor removal 99
- Chapter 6 Artificial intelligence in breast cancer management 121
- Chapter 7 AI innovations in colorectal cancer detection and treatment 143
- Chapter 8 AI applications in brain cancer therapy 165
- Chapter 9 Leveraging AI for liver cancer diagnosis and treatment 191
- Chapter 10 AI-driven advances in lung cancer care 213
- Chapter 11 Artificial intelligence in prostate cancer detection and management 243
- Chapter 12 AI solutions for skin cancer diagnosis and treatment 265
- Index 345
- De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- About the authors XVII
- Chapter 1 Artificial intelligence in cancer treatment and management 1
- Chapter 2 AI-based approaches to cancer drug discovery 27
- Chapter 3 Integrating AI and digital twin technology in cancer therapy 47
- Chapter 4 AI for enhanced cancer detection and diagnosis 63
- Chapter 5 AI-guided surgical interventions for cancer and tumor removal 99
- Chapter 6 Artificial intelligence in breast cancer management 121
- Chapter 7 AI innovations in colorectal cancer detection and treatment 143
- Chapter 8 AI applications in brain cancer therapy 165
- Chapter 9 Leveraging AI for liver cancer diagnosis and treatment 191
- Chapter 10 AI-driven advances in lung cancer care 213
- Chapter 11 Artificial intelligence in prostate cancer detection and management 243
- Chapter 12 AI solutions for skin cancer diagnosis and treatment 265
- Index 345
- De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences
