Bounded Rationality in AI-Assisted Medical Decision-Making

Bounded Rationality in AI-Assisted Medical Decision-Making | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bounded Rationality in AI-Assisted Medical Decision-Making Gyula Seres, Lawrence Jin, Yoann Sapanel, Dean Ho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7813323/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent advances in generative AI models enabled the creation of digital health assistants for patients. However, it remains unclear how patients - especially in the presence of cognitive biases - would utilize them. Drawing on behavioral decision theory (BDT), we analyzed how bounded rational patients use AI health assistants to make healthcare choices. Our findings show that cognitive biases lead patients to underutilize these assistants, limiting their potential to prompt high-risk patients to seek necessary care and to reduce unnecessary clinical visits among low-risk patients. Moreover, we found that bounded rational patients become less sensitive to differences in risk, and their decision to seek clinical care is determined primarily by the cost of access to healthcare rather than by the underlying health risk. These findings highlight the need for developers to design bias-mitigating interfaces and general transparency in the model, and for policymakers to establish safeguards to support effective adoption of these technologies. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7813323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526871500,"identity":"ae17d18e-7a29-4c6b-9484-adc19c73114b","order_by":0,"name":"Gyula Seres","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYHACxgMMFRCWBNF6DjCcIVkLYxspWuQjcgwO/Jx3J3F+A/PB2zwMdXYNhLQY3sgxONi77VnihgNsydY8DIeTCWuZAbSFd9vhxA0MPGbSPAwHkgk6DKTl4N85h4EO4/8G1FJHWIu8RI7BYd6Gw4kNB3jYgFqY7QhqMeB5VnBY5thh4w2H2Ywt5xgcTiBsS3vyxodvag7Lzm9vfnjjTUWdPWFbLsCMZQZzGRIbCNrSfwBVgLAto2AUjIJRMOIAAJVxP5gsFNzbAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4254-0198","institution":"National University of Singapore","correspondingAuthor":true,"prefix":"","firstName":"Gyula","middleName":"","lastName":"Seres","suffix":""},{"id":526871501,"identity":"717a5ecb-3adf-4a29-a4b1-dcae4745970c","order_by":1,"name":"Lawrence Jin","email":"","orcid":"https://orcid.org/0000-0003-3679-874X","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"","lastName":"Jin","suffix":""},{"id":526871502,"identity":"54794edd-5a97-42e1-9d0b-113585c582f1","order_by":2,"name":"Yoann Sapanel","email":"","orcid":"https://orcid.org/0000-0001-6797-7850","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Yoann","middleName":"","lastName":"Sapanel","suffix":""},{"id":526871503,"identity":"1e2de94f-0210-4c10-b087-8cc8c9fe6e3a","order_by":3,"name":"Dean Ho","email":"","orcid":"https://orcid.org/0000-0002-7337-296X","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Dean","middleName":"","lastName":"Ho","suffix":""}],"badges":[],"createdAt":"2025-10-09 05:47:50","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7813323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7813323/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93192592,"identity":"dd885305-a5d9-4d85-9ff9-3b80eb2a576c","added_by":"auto","created_at":"2025-10-10 04:48:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":661291,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7813323/v1_covered_9db95926-709b-4f4a-bbe6-a44d94c850f3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBounded Rationality in AI-Assisted Medical Decision-Making\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7813323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7813323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent advances in generative AI models enabled the creation of digital health assistants for patients. However, it remains unclear how patients - especially in the presence of cognitive biases - would utilize them. Drawing on behavioral decision theory (BDT), we analyzed how bounded rational patients use AI health assistants to make healthcare choices. Our findings show that cognitive biases lead patients to underutilize these assistants, limiting their potential to prompt high-risk patients to seek necessary care and to reduce unnecessary clinical visits among low-risk patients. Moreover, we found that bounded rational patients become less sensitive to differences in risk, and their decision to seek clinical care is determined primarily by the cost of access to healthcare rather than by the underlying health risk. These findings highlight the need for developers to design bias-mitigating interfaces and general transparency in the model, and for policymakers to establish safeguards to support effective adoption of these technologies.\u003c/p\u003e","manuscriptTitle":"Bounded Rationality in AI-Assisted Medical Decision-Making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 04:40:06","doi":"10.21203/rs.3.rs-7813323/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41078196-472b-4717-bcbc-8556189f2991","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-10T04:40:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 04:40:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7813323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7813323","identity":"rs-7813323","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}