ChatGPT shows promise in addressing heart failure queries with accuracy and precision

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ChatGPT shows promise in addressing heart failure queries with accuracy and precision
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ChatGPT shows promise in addressing heart failure queries with accuracy and precision UCIrvine CedarsSinai CharGPT heartfailure health AI artificialintelligence

By Susha Cheriyedath, M.Sc.Jul 14 2023Reviewed by Benedette Cuffari, M.Sc. In a recent study posted to the medRxiv* preprint server, researchers evaluate the accuracy and reproducibility of responses from ChatGPT versions 3.5 and 4 in answering heart failure-related questions.

Background By 2030, researchers estimate that healthcare costs associated with heart failure will reach around $70 billion USD every year in the United States. About 70% of these costs are due to hospitalizations, which constitute 1-2% of all hospital admissions in the United States. Studies have shown that patients who possess more knowledge about managing their heart condition tend to have fewer and shorter hospital stays.

About the study In the current study, researchers collected a list of 125 commonly asked questions about heart failure from reputable medical organizations and Facebook support groups. After careful evaluation, 18 questions with duplicate content, vague phrasing, or did not address the patient’s perspective were eliminated.

Any discrepancies in grading between the reviewers were resolved by a third reviewer who is a board-certified specialist in advanced heart failure with over 20 years of clinical experience. About 2% of responses from ChatGPT-3.5 was graded as ‘some correct and some incorrect,’ while no responses from ChatGPT-4 fell into this category or the ‘completely incorrect’ category. When examining reproducibility, both models provided consistent responses for most questions, with the ChatGPT-3.5 version scoring more than 94% in all categories and GPT-4 achieving 100% reproducibility for all answers.

While ChatGPT performed well in this study, there are important limitations to consider. Occasionally, the model may provide inaccurate but believable responses and, at times, nonsensical answers.

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