Security Implications of AI Chatbots in Health Care.

AI ChatGPT HIPAA artificial intelligence care chatbot computer program data security guidelines health information improvement natural language processing patient care policy privacy risk security tool

Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
28 Nov 2023
Historique:
received: 24 03 2023
accepted: 20 11 2023
revised: 30 08 2023
medline: 29 11 2023
pubmed: 28 11 2023
entrez: 28 11 2023
Statut: epublish

Résumé

Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people's data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care.

Identifiants

pubmed: 38015597
pii: v25i1e47551
doi: 10.2196/47551
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e47551

Informations de copyright

©Jingquan Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.11.2023.

Références

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pubmed: 26155953
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pubmed: 33764885
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pubmed: 23599228
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pubmed: 37606976
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Auteurs

Jingquan Li (J)

Hofstra University, Hempstead, NY, United States.

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Classifications MeSH