Artificial Intelligence in Medicine: Technical Basis and Clinical Applications

Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI.

The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine.

Provides history and overview of artificial intelligence, as narrated by pioneers in the fieldDiscusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligenceAddresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

I Introduction

1. Artificial intelligence in medicine: past, present, and future 2. Artificial intelligence in medicine: Technical basis and clinical applications

II Technical basis

3. Deep learning for biomedical videos: perspective and recommendations 4. Biomedical imaging and analysis through deep learning 5. Expert systems in medicine 6. Privacy-preserving collaborative deep learning methods for multiinstitutional training without sharing patient data 7. Analytics methods and tools for integration of biomedical data in medicine

III Clinical applications

8. Electronic health record data mining for artificial intelligence healthcare 9. Roles of artificial intelligence in wellness, healthy living, and healthy status sensing 10. The growing significance of smartphone apps in data-driven clinical decision-making: Challenges and pitfalls 11. Artifical intelligence for pathology 12. The potential of deep learning for gastrointestinal endoscopy-a disruptive new technology 13. Lessons learnt from harnessing deep learning for real-world clinical applications in ophthalmology: detecting diabetic retinopathy from retinal fundus photographs 14. Artificial intelligence in radiology 15. Artificial intelligence and interpretations in breast cancer imaging 16.  Prospect and adversity of artificial intelligence in urology 17.  Meaningful incorporation of artificial intelligence for personalized patient management during cancer: Quantitative imaging, risk assessment, and therapeutic outcomes 18. Artificial intelligence in oncology 19. Artificial intelligence in cardiovascular imaging 20. Artificial intelligence as applied to clinical neurological conditions 21. Harnessing the potential of artificial neural networks for pediatric patient management 22. Artificial intelligence—enabled public health surveillance—from local detection to global epidemic monitoring and control

IV Future outlook

23. Regulatory, social, ethical, and legal issues of artificial intelligence in medicine 24. Industry perspectives and commercial opportunities of artificial intelligence in medicine 25. Outlook of the future landscape of artificial intelligence in medicine and new challenges