Literature DB >> 33822422

Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.

George Maliha1,2, Sara Gerke3, I Glenn Cohen3,4, Ravi B Parikh1,2,5,6.   

Abstract

Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability. While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence. Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no-fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care.
© 2021 Milbank Memorial Fund.

Entities:  

Keywords:  Machine learning; artificial intelligence; liability; malpractice

Mesh:

Year:  2021        PMID: 33822422      PMCID: PMC8452365          DOI: 10.1111/1468-0009.12504

Source DB:  PubMed          Journal:  Milbank Q        ISSN: 0887-378X            Impact factor:   6.237


  33 in total

1.  Hospital liability for negligence of independent contractor physicians under principles of apparent agency.

Authors:  Adam Alstott
Journal:  J Leg Med       Date:  2004-12

2.  Medical malpractice in twentieth century United States. The interaction of technology, law and culture.

Authors:  K De Ville
Journal:  Int J Technol Assess Health Care       Date:  1998       Impact factor: 2.188

3.  A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Authors:  Curtis P Langlotz; Bibb Allen; Bradley J Erickson; Jayashree Kalpathy-Cramer; Keith Bigelow; Tessa S Cook; Adam E Flanders; Matthew P Lungren; David S Mendelson; Jeffrey D Rudie; Ge Wang; Krishna Kandarpa
Journal:  Radiology       Date:  2019-04-16       Impact factor: 11.105

Review 4.  Hypertension Management in Older and Frail Older Patients.

Authors:  Athanase Benetos; Mirko Petrovic; Timo Strandberg
Journal:  Circ Res       Date:  2019-03-29       Impact factor: 17.367

5.  Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data.

Authors:  Milena A Gianfrancesco; Suzanne Tamang; Jinoos Yazdany; Gabriela Schmajuk
Journal:  JAMA Intern Med       Date:  2018-11-01       Impact factor: 21.873

6.  The jury is still in: Florida's Birth-Related Neurological Injury Compensation Plan after a decade.

Authors:  D M Studdert; L A Fritz; T A Brennan
Journal:  J Health Polit Policy Law       Date:  2000-06       Impact factor: 2.265

7.  Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

Authors:  Marco V Perez; Kenneth W Mahaffey; Haley Hedlin; John S Rumsfeld; Ariadna Garcia; Todd Ferris; Vidhya Balasubramanian; Andrea M Russo; Amol Rajmane; Lauren Cheung; Grace Hung; Justin Lee; Peter Kowey; Nisha Talati; Divya Nag; Santosh E Gummidipundi; Alexis Beatty; Mellanie True Hills; Sumbul Desai; Christopher B Granger; Manisha Desai; Mintu P Turakhia
Journal:  N Engl J Med       Date:  2019-11-14       Impact factor: 176.079

8.  Artificial intelligence, bias and clinical safety.

Authors:  Robert Challen; Joshua Denny; Martin Pitt; Luke Gompels; Tom Edwards; Krasimira Tsaneva-Atanasova
Journal:  BMJ Qual Saf       Date:  2019-01-12       Impact factor: 7.035

9.  Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.

Authors:  Michael D Abràmoff; Philip T Lavin; Michele Birch; Nilay Shah; James C Folk
Journal:  NPJ Digit Med       Date:  2018-08-28

Review 10.  The "inconvenient truth" about AI in healthcare.

Authors:  Trishan Panch; Heather Mattie; Leo Anthony Celi
Journal:  NPJ Digit Med       Date:  2019-08-16
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