Evaluating Recommendations About Atrial Fibrillation for Patients and Clinicians Obtained From Chat-Based Artificial Intelligence Algorithms

HomeCirculation: Arrhythmia and ElectrophysiologyVol. 16, No. 7Evaluating Recommendations About Atrial Fibrillation for Patients and Clinicians Obtained From Chat-Based Artificial Intelligence Algorithms Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBEvaluating Recommendations About Atrial Fibrillation for Patients and Clinicians Obtained From Chat-Based Artificial Intelligence Algorithms Zahra Azizi, Pouria Alipour, Sofia Gomez, Cassandra Broadwin, Sumaiya Islam, Ashish Sarraju, A.J. Rogers, Alexander T. Sandhu and Fatima Rodriguez Zahra AziziZahra Azizi https://orcid.org/0000-0002-7897-0934 Center for Digital Health (Z.A., C.B., S.I.). , Pouria AlipourPouria Alipour https://orcid.org/0000-0002-0751-2995 Department of Medicine, McGill University, Montreal, QC, Canada (P.A.). , Sofia GomezSofia Gomez Department of Medicine, Stanford School of Medicine, CA (S.G.). , Cassandra BroadwinCassandra Broadwin Center for Digital Health (Z.A., C.B., S.I.). , Sumaiya IslamSumaiya Islam https://orcid.org/0000-0001-6901-9883 Center for Digital Health (Z.A., C.B., S.I.). , Ashish SarrajuAshish Sarraju https://orcid.org/0000-0003-1649-2110 Department of Cardiovascular Medicine, Cleveland Clinic, OH (A.S.). , A.J. RogersA.J. Rogers https://orcid.org/0000-0001-6585-534X Stanford University Division of Cardiovascular Medicine & Cardiovascular Institute, Department of Medicine, Stanford University, CA (A.J.R., A.T.S., F.R.). , Alexander T. SandhuAlexander T. Sandhu https://orcid.org/0000-0003-3208-1143 Stanford University Division of Cardiovascular Medicine & Cardiovascular Institute, Department of Medicine, Stanford University, CA (A.J.R., A.T.S., F.R.). Department of Cardiovascular Medicine, Cleveland Clinic, OH (A.S.). and Fatima RodriguezFatima Rodriguez Correspondence to: Fatima Rodriguez, MD, MPH, Division of Cardiovascular Medicine, Stanford University, Mail Code 5687, 453 Quarry Rd, Center for Academic Medicine, Stanford, CA 94304. Email E-mail Address: [email protected] https://orcid.org/0000-0002-5226-0723 Stanford University Division of Cardiovascular Medicine & Cardiovascular Institute, Department of Medicine, Stanford University, CA (A.J.R., A.T.S., F.R.). Originally published19 Jun 2023https://doi.org/10.1161/CIRCEP.123.012015Circulation: Arrhythmia and Electrophysiology. 2023;16:415–417Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: June 19, 2023: Ahead of Print Atrial fibrillation (AF) is the most common arrhythmia worldwide.1 Crowdsourced information and peer-to-peer recommendations online have resulted in the dissemination of sometimes inaccurate information about AF prevention and management.1–3 With the increasing availability of artificial intelligence (AI) and machine learning technologies at the consumer level, patients are likely turn to such modalities for medical guidance.1–3 However, the appropriateness of the advice provided about AF from this chat-based AI models has not yet been investigated. Recently, Sarraju et al4 evaluated the appropriateness of ChatGPT’s responses to various questions preventive cardiologists commonly encounter in clinical settings. We extended the work by evaluating performance differences between patient and clinician questions about AF using 2 popular chat-based language models (ChatGPT and Bing AI).Eighteen prompts were designed in consultation with clinicians who have expertise in managing AF to reflect questions that patients commonly ask about AF (using public and free version of ChatGPT v3.5), while another 18 prompts reflected the perspectives of clinicians (using both ChatGPT and Bing AI [GPT-4]). Each question was input into each platform 3 times (Figure).4 Patients’ questions were classified into 4 major categories (overall AF, treatments, medications, and daily lifestyle). Clinician questions began with the prompt, “I am a physician,” and included the phrases “based on the most recent guidelines” and “with reference” to elicit higher-level scientific or medical responses from the AI engines.Download figureDownload PowerPointFigure. Evaluation of atrial fibrillation recommendations to patients and clinicians from online chat-based artificial intelligence algorithms. Patient-facing and clinician-facing questions as input into the AI tools are listed above, with patient-facing questions categorized by question topic (overall, treatment, medication, and lifestyle). Each question was asked 3 times to ensure the consistency of responses. In case of inquiries by clinicians, we asked the machine to provide evidence and citations for the given recommendation. The output was considered appropriate only if the given response (in all 3 instances) along with its evidence (in case of clinician-related questions) were correct and based on the most recent evidence and guidelines. Each set of responses was graded by 2 clinicians (cardiologist, electrophysiologist) and in case of disagreement, the results were checked by a third clinician. The reviewers’ agreement was between 89-100% in all cases. Cohen’s Kappa for ChatGPT was between 0.77 to 1 (ChatGPT: Text=1, Reference=0.77; Bing AI:Text=0.77, Reference=0.77). AF indicates atrial fibrillation; AFL, atrial flutter; AI, artificial intelligence; and LAA, left atrial appendage.Each set of responses was reviewed by 3 expert clinicians who categorized them as either appropriate or inappropriate. The agreement between reviewers was determined by calculating Cohen’s Kappa statistics. An appropriate response was one that not only contained accurate information, but also employed language deemed to be clear and comprehensible to the user (patient/general public or clinician) given different levels of assumed medical literacy. Clinician question responses were labeled inappropriate if they were either incorrect based on expert judgement or contained inaccurate references or language that was deemed to be unclear. The data that support the findings of this study are available from ZA (zazizi@stanford.edu) and upon reasonable request and ZA takes responsibility for its integrity. This study is not subject to institutional review board approval or request for exemption as no human or animal subjects were utilized.The overall appropriateness of responses is displayed in the Figure. The level of agreement between reviewers was determined to have a kappa statistic of 0.77 to 1, indicating high agreement. Overall, 83.3% (overall AF 83.3%, treatments 100%, medications 100%, and daily lifestyle 71.4%) of patient-initiated prompts had appropriate responses generated by ChatGPT. However, ChatGPT yielded some inaccurate responses to some questions, particularly those pertaining to triggers of AF, such as alcohol and coffee (Figure). For clinician-related questions, the text accuracy of ChatGPT and Bing AI responses was 33.3% and 66.6%, while reference accuracy was 55.5% and 50%, respectively. In cases of appropriate responses, while most cited references included current US and European guidelines, the algorithms were also able to provide responses using appropriately cited primary literature. However, in 2 instances relating to AF management, ChatGPT confabulated references and trials that did not exist.Our findings demonstrate that while ChatGPT may appropriately summarize certain general information regarding AF for patients, it is not completely accurate. Moreover, we found that ChatGPT and Bing AI lacked accuracy and did not provide appropriate scientific basis for recommendations aimed at health care professionals, suggesting that these tools may be unsuitable for clinical decision-making or research.Notable shortcomings of ChatGPT for patient questions included the inability to provide correct responses to AF triggers such as alcohol and coffee consumption. These are nuanced topics with varying opinions in the medical literature, which may contribute to the disagreements seen. Among the accurate responses provided by ChatGPT and Bing AI for clinician focused queries, certain cited references were fictitious or incorrectly cited, commonly called AI hallucinations.5 Compared with ChatGPT, Bing AI had higher response accuracy but a comparable frequency of inaccurate references.One limitation of this study is the use of AI chatbots designed for the general public rather than medical applications. Should the algorithms be trained on health care data, it is possible that the accuracy and scientific merit of responses to clinician queries would improve.In conclusion, current AI chatbots may be a useful tool to provide a cursory overview of diseases for patients and the general public. However, this tool showed limited appropriateness of clinician-facing questions in this study. This tech is rapidly evolving and future models may be updated real time to avoid some of the wrong answers due to outdated information. Finally, it is imperative to note that this tool is not able to replace a human’s intuition, situational awareness, and, most importantly, clinical judgment. Much greater accuracy and clinical prowess are expected of clinicians managing diseases such as atrial fibrillation.ARTICLE INFORMATIONSources of FundingDr Rodriguez was funded by grants from the NIH National Heart, Lung, and Blood Institute (1K01HL144607), the American Heart Association/Harold Amos Faculty Development program, and the Doris Duke Foundation (Grant No. 2022051). Dr Azizi was funded by the American Heart Association Health Tech SFRN Fellowship.Nonstandard Abbreviations and AcronymsAFatrial fibrillationAIartificial intelligenceDisclosures Dr Rodriguez reports consulting relationships with HealthPals, Novartis, Amgen, NovoNordisk (CEC), and AstraZeneca. The remaining authors have nothing to disclose.FootnotesThis article was sent to Andrew E. Epstein, MD, Guest Editor, for review by expert referees, editorial decision, and final disposition.For Sources of Funding and Disclosures, see page 416.Correspondence to: Fatima Rodriguez, MD, MPH, Division of Cardiovascular Medicine, Stanford University, Mail Code 5687, 453 Quarry Rd, Center for Academic Medicine, Stanford, CA 94304. Email frodrigu@stanford.eduReferences1. Wilson RE, Rush KL. Engaging Patients in Atrial Fibrillation Self-Care: Opportunities for Future Research. In: Los Angeles, CA: SAGE Publications Sage CA; 2021:1091–1092.Google Scholar2. Palm P, Qvist I, Rasmussen TB, Christensen SW, Håkonsen SJ, Risom SS. Educational interventions to improve outcomes in patients with atrial fibrillation—a systematic review.Int J Clin Pract. 2020; 74:e13629. doi: 10.1111/ijcp.13629CrossrefMedlineGoogle Scholar3. Mann DL. Artificial intelligence discusses the role of artificial intelligence in translational medicine.JACC Basic Transl Sci. 2023; 8:221–223. doi: 10.1016/j.jacbts.2023.01.001CrossrefMedlineGoogle Scholar4. Sarraju A, Bruemmer D, Van Iterson E, Cho L, Rodriguez F, Laffin L. Appropriateness of cardiovascular disease prevention recommendations obtained from a popular online chat-based artificial intelligence model.JAMA. 2023; 329:842–844. doi: 10.1001/jama.2023.1044CrossrefMedlineGoogle Scholar5. Alkaissi H, McFarlane SI. Artificial hallucinations in ChatGPT: implications in scientific writing.Cureus. 2023; 15:e35179. doi: 10.7759/cureus.35179CrossrefMedlineGoogle Scholar eLetters(0)eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.Sign In to Submit a Response to This Article Previous Back to top Next FiguresReferencesRelatedDetailsCited By Hillmann H, Angelini E, Karfoul N, Feickert S, Mueller-Leisse J and Duncker D (2023) Accuracy and comprehensibility of chat-based artificial intelligence for patient information on atrial fibrillation and cardiac implantable electronic devices, Europace, 10.1093/europace/euad369, 26:1, Online publication date: 28-Dec-2023. July 2023Vol 16, Issue 7 Advertisement Article InformationMetrics © 2023 American Heart Association, Inc.https://doi.org/10.1161/CIRCEP.123.012015PMID: 37334705 Originally publishedJune 19, 2023 Keywordsarrhythmiaatrial fibrillationpatientsphysiciansPDF download Advertisement SubjectsAtrial FibrillationMachine Learning and Artificial Intelligence