[HTML][HTML] Artificial intelligence in health informatics: hype or reality?
KF Hollis, LF Soualmia… - Yearbook of medical …, 2019 - thieme-connect.com
Yearbook of medical informatics, 2019•thieme-connect.com
Objectives: To provide an introduction to the 2019 International Medical Informatics
Association (IMIA) Yearbook by the editors. Methods: This editorial presents an overview
and introduction to the 2019 IMIA Yearbook which includes the special topic “Artificial
Intelligence in Health: New Opportunities, Challenges, and Practical Implications". The
special topic is discussed, the IMIA President's statement is introduced, and changes in the
Yearbook editorial team are described. Results: Artificial intelligence (AI) in Medicine arose …
Association (IMIA) Yearbook by the editors. Methods: This editorial presents an overview
and introduction to the 2019 IMIA Yearbook which includes the special topic “Artificial
Intelligence in Health: New Opportunities, Challenges, and Practical Implications". The
special topic is discussed, the IMIA President's statement is introduced, and changes in the
Yearbook editorial team are described. Results: Artificial intelligence (AI) in Medicine arose …
Objectives: To provide an introduction to the 2019 International Medical Informatics Association (IMIA) Yearbook by the editors.
Methods: This editorial presents an overview and introduction to the 2019 IMIA Yearbook which includes the special topic “Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications". The special topic is discussed, the IMIA President’s statement is introduced, and changes in the Yearbook editorial team are described.
Results: Artificial intelligence (AI) in Medicine arose in the 1970’s from new approaches for representing expert knowledge with computers. Since then, AI in medicine has gradually evolved toward essentially data-driven approaches with great results in image analysis. However, data integration, storage, and management still present clear challenges among which the lack of explanability of the results produced by data-driven AI methods.
Conclusion: With more health data availability, and the recent developments of efficient and improved machine learning algorithms, there is a renewed interest for AI in medicine.
The objective is to help health professionals improve patient care while also reduce costs. However, the other costs of AI, including ethical issues when processing personal health data by algorithms, should be included.
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