Artificial intelligence methods in computer-aided diagnostic tools and decision support analytics for clinical informatics

Author(s):  
Khajamoinuddin Syed ◽  
William C. Sleeman ◽  
Joseph J. Nalluri ◽  
Rishabh Kapoor ◽  
Michael Hagan ◽  
...  
2020 ◽  
Vol 93 (1106) ◽  
pp. 20190855 ◽  
Author(s):  
Issam El Naqa ◽  
Masoom A Haider ◽  
Maryellen L Giger ◽  
Randall K Ten Haken

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI’s inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI’s chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.


1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.


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