scholarly journals A riddle, wrapped in a mystery, inside an enigma: How semantic black boxes and opaque artificial intelligence confuse medical decision‐making

Bioethics ◽  
2021 ◽  
Author(s):  
Robin Pierce ◽  
Sigrid Sterckx ◽  
Wim Van Biesen
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Black Boxes

scholarly journals Technology of Supporting Medical Decision-Making Using Evidence-Based Medicine and Artificial Intelligence

2020 ◽  
Vol 176 ◽  
pp. 1703-1712
Author(s):  
Georgy Lebedev ◽  
Eduard Fartushnyi ◽  
Igor Fartushnyi ◽  
Igor Shaderkin ◽  
Herman Klimenko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Evidence Based Medicine ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Evidence Based ◽  
Based Medicine

Limits of trust in medical AI

2020 ◽  
Vol 46 (7) ◽  
pp. 478-481 ◽  
Author(s):  
Joshua James Hatherley
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Clinical Practice ◽  
Medical Decision Making ◽  
Hospital Readmissions ◽  
New Drugs ◽  
Medical Decision ◽  
Practice Of Medicine

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.

May We Support Your Decision?

1996 ◽  
Vol 1 (3) ◽  
pp. 175-178 ◽  
Author(s):  
Colin Gordon
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Clinical Practice ◽  
Decision Support ◽  
Health Care Professionals ◽  
Medical Decision Making ◽  
Electronic Patient Record ◽  
Medical Decision ◽  
Technical Developments

Expert systems to support medical decision-making have so far achieved few successes. Current technical developments, however, may overcome some of the limitations. Although there are several theoretical currents in medical artificial intelligence, there are signs of them converging. Meanwhile, decision support systems, which set themselves more modest goals than replicating or improving on clinicians' expertise, have come into routine use in places where an adequate electronic patient record exists. They may also be finding a wider role, assisting in the implementation of clinical practice guidelines. There is, however, still much uncertainty about the kinds of decision support that doctors and other health care professionals are likely to want or accept.

scholarly journals Artificial Intelligence in Cardiac Imaging

2020 ◽  
Vol 13 (2) ◽  
pp. 110-116 ◽  
Author(s):  
Karthik Seetharam ◽  
Sirish Shrestha ◽  
Partho P Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Cardiac Imaging ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Precision Phenotyping ◽  
Cardiac Disorders ◽  
Enhance Patient Care

Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.

scholarly journals Liability issues for the use of artificial intelligence in health care in Canada: AI and medical decision-making

2020 ◽  
Vol 46 (2) ◽  
Author(s):  
Mélanie Bourassa Forcier ◽  
Lara Khoury ◽  
Nathalie Vézina
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Medical Decision Making ◽  
Legal Framework ◽  
Medical Decision

This paper explores Canadian liability concerns flowing from the integration of artificial intelligence (AI) as a tool assisting physicians in their medical decision-making. It argues that the current Canadian legal framework is sufficient, in most cases, to allow developers and users of AI technology to assess each stakeholder's responsibility should the technology cause harm.

scholarly journals role of cognitive science and artificial intelligence in supporting clinical diagnosis

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Claudio Lucchiari ◽  
Maria Elide Vanutelli ◽  
Raffaella Folgieri
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Medical Decision Making ◽  
Diagnostic Errors ◽  
Medical Decision ◽  
Future Developments ◽  
Critical Issues

Research suggests that doctors are failing to make use of technologies designed to optimize their decision-making skills in daily clinical activities, despite a proliferation of electronic tools with the potential for decreasing risks of medical and diagnostic errors. This paper addresses this issue by exploring the cognitive basis of medical decision making and its psychosocial context in relation to technology. We then discuss how cognitive-led technologies – in particular, decision support systems and artificial neural networks – may be applied in clinical contexts to improve medical decision making without becoming a substitute for the doctor’s judgment. We identify critical issues and make suggestions regarding future developments.

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