Autonomous AI systems in the face of liability, regulations and costs

Agustina D. Saenz; Zach Harned; Oishi Banerjee; Michael D. Abràmoff; Pranav Rajpurkar

doi:10.1038/s41746-023-00929-1

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Autonomous AI systems in the face of liability, regulations and costs

Journal article

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Peer reviewed

Autonomous AI systems in the face of liability, regulations and costs

Agustina D. Saenz, Zach Harned, Oishi Banerjee, Michael D. Abràmoff and Pranav Rajpurkar

NPJ digital medicine, Vol.6(1), 185

10/06/2023

DOI: 10.1038/s41746-023-00929-1

PMCID: PMC10558567

PMID: 37803209

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Abstract

Autonomous AI systems in medicine promise improved outcomes but raise concerns about liability, regulation, and costs. With the advent of large-language models, which can understand and generate medical text, the urgency for addressing these concerns increases as they create opportunities for more sophisticated autonomous AI systems. This perspective explores the liability implications for physicians, hospitals, and creators of AI technology, as well as the evolving regulatory landscape and payment models. Physicians may be favored in malpractice cases if they follow rigorously validated AI recommendations. However, AI developers may face liability for failing to adhere to industry-standard best practices during development and implementation. The evolving regulatory landscape, led by the FDA, seeks to ensure transparency, evaluation, and real-world monitoring of AI systems, while payment models such as MPFS, NTAP, and commercial payers adapt to accommodate them. The widespread adoption of autonomous AI systems can potentially streamline workflows and allow doctors to concentrate on the human aspects of healthcare.

Details

Title: Subtitle: Autonomous AI systems in the face of liability, regulations and costs
Creators: Agustina D. Saenz - Harvard University
Zach Harned - Association for the Advancement of Artificial Intelligence
Oishi Banerjee - Harvard University
Michael D. Abràmoff - University of Iowa
Pranav Rajpurkar - Harvard University
Resource Type: Journal article
Publication Details: NPJ digital medicine, Vol.6(1), 185
DOI: 10.1038/s41746-023-00929-1
PMID: 37803209
PMCID: PMC10558567
NLM abbreviation: NPJ Digit Med
ISSN: 2398-6352
eISSN: 2398-6352
Publisher: Nature Publishing Group UK
Language: English
Date published: 10/06/2023
Academic Unit: Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984474571302771

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