Literature DB >> 34511267

The three ghosts of medical AI: Can the black-box present deliver?

Thomas P Quinn1, Stephan Jacobs2, Manisha Senadeera2, Vuong Le2, Simon Coghlan3.   

Abstract

Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in A Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article takes readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability and success of medical AI.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Autonomy; Black-box; Challenges; Ethics; Transparency; Xai

Mesh:

Year:  2021        PMID: 34511267     DOI: 10.1016/j.artmed.2021.102158

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  9 in total

Review 1.  Trustworthy Artificial Intelligence in Medical Imaging.

Authors:  Navid Hasani; Michael A Morris; Arman Rhamim; Ronald M Summers; Elizabeth Jones; Eliot Siegel; Babak Saboury
Journal:  PET Clin       Date:  2022-01

Review 2.  Artificial Intelligence Applications in Health Care Practice: Scoping Review.

Authors:  Malvika Sharma; Carl Savage; Monika Nair; Ingrid Larsson; Petra Svedberg; Jens M Nygren
Journal:  J Med Internet Res       Date:  2022-10-05       Impact factor: 7.076

3.  Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework.

Authors:  Dhakshenya Ardhithy Dhinagaran; Laura Martinengo; Moon-Ho Ringo Ho; Shafiq Joty; Tobias Kowatsch; Rifat Atun; Lorainne Tudor Car
Journal:  JMIR Mhealth Uhealth       Date:  2022-10-04       Impact factor: 4.947

4.  Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration.

Authors:  Dan J Stein; Steven J Shoptaw; Daniel V Vigo; Crick Lund; Pim Cuijpers; Jason Bantjes; Norman Sartorius; Mario Maj
Journal:  World Psychiatry       Date:  2022-10       Impact factor: 79.683

5.  Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond.

Authors:  Guang Yang; Qinghao Ye; Jun Xia
Journal:  Inf Fusion       Date:  2022-01       Impact factor: 12.975

6.  Ghost in the machine or monkey with a typewriter-generating titles for Christmas research articles in The BMJ using artificial intelligence: observational study.

Authors:  Robin Marlow; Dora Wood
Journal:  BMJ       Date:  2021-12-15

7.  Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study.

Authors:  Tom Finck; Hongwei Li; Sarah Schlaeger; Lioba Grundl; Nico Sollmann; Benjamin Bender; Eva Bürkle; Claus Zimmer; Jan Kirschke; Björn Menze; Mark Mühlau; Benedikt Wiestler
Journal:  Front Neurosci       Date:  2022-04-26       Impact factor: 5.152

8.  Ethics of AI in Radiology: A Review of Ethical and Societal Implications.

Authors:  Melanie Goisauf; Mónica Cano Abadía
Journal:  Front Big Data       Date:  2022-07-14

9.  Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram.

Authors:  Rafael Gonzalez-Landaeta; Brenda Ramirez; Jose Mejia
Journal:  Sci Rep       Date:  2022-10-13       Impact factor: 4.996

  9 in total

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