{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:08:20Z","timestamp":1782846500241,"version":"3.54.5"},"reference-count":43,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Aging"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Prostate\u2011specific antigen (PSA) screening involves complex trade\u2011offs between early detection and the risks of overdiagnosis. For older adults (aged \u226550 years), shared decision\u2011making (SDM) is often hindered by limited health literacy, sensory or cognitive impairments, and multimorbidity, which complicate risk comprehension. Traditional decision aids provide foundational knowledge but are often nonpersonalized. Machine learning (ML) may offer individualized recommendations, yet the psychological and behavioral effects of ML\u2011assisted SDM in geriatric populations remain poorly characterized.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to develop and evaluate a web\u2011based, ML\u2011driven decision aid integrated into an SDM workflow to provide personalized PSA screening recommendations and to assess its effects on decisional conflict (primary outcome), state anxiety, and decision satisfaction among middle\u2011aged and older men.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The study followed a 2\u2011stage design. First, a model establishment group (n=507) was used to train and evaluate 6 ML algorithms based on clinical and values\u2011clarification data. A random forest model was selected for its superior performance (mean area under the curve 0.933, SD 0.350; 95% CI 0.902-0.963). Second, a randomized controlled trial was conducted with 367 participants (mean age 64.34, SD 10.30 years) randomly assigned 1:1 to the ML suggestion group (MLSG; n=185) or the control group (CG; n=182). Both groups received video\u2011based education, counseling, and values clarification; only the MLSG received an ML\u2011generated \u201csecond opinion\u201d recommendation. Primary and secondary outcomes were assessed using the Decisional Conflict Scale (DCS), Spielberger State\u2011Trait Anxiety Inventory (STAI), and Satisfaction with Decision scale.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n In the randomized controlled trial (n=367), the MLSG reported significantly lower decisional conflict than the CG (total DCS score: mean difference [MD] \u20133.77, 95% CI \u20135.55 to \u22121.99; Cohen\n d<\/jats:italic>\n =\u20130.44; P<.001). The MLSG reported greater perceived support (DCS7: adjusted\n P<\/jats:italic>\n =.03), more adequate advice (DCS9: adjusted\n P<\/jats:italic>\n <.001), and higher decision confidence (DCS10: adjusted\n P<\/jats:italic>\n =.03; DCS11: adjusted\n P<\/jats:italic>\n <.001). Regarding psychological well\u2011being, although total anxiety scores did not differ, the MLSG reported reduced worry (STAI item 6: MD \u20130.98, 95% CI \u20131.20 to \u22120.76;\n d<\/jats:italic>\n =\u20130.89; adjusted\n P<\/jats:italic>\n <.001) and increased calmness (STAI item 1: MD 0.30, 95% CI 0.06-0.54;\n d<\/jats:italic>\n =0.25; adjusted\n P<\/jats:italic>\n =.01). Decision satisfaction was higher in the MLSG across all items (total Satisfaction with Decision score: MD \u20137.38, 95% CI \u20138.54 to \u22126.18;\n P<\/jats:italic>\n <.001). Behavioral choices were strongly influenced by the ML recommendation: participants in the MLSG who received an \u201caccept\u201d recommendation were more likely to select \u201caccept\u201d (34\/67, 50.7%) than those in the CG (44\/182, 24.2%;\n P<\/jats:italic>\n <.001). When the system suggested \u201cnot now,\u201d only 17.8% (21\/118) chose \u201caccept,\u201d which was lower than in the CG.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Integrating personalized ML recommendations into SDM workflows provides emotional scaffolding for older men, reducing decisional distress and enhancing confidence without undermining autonomy. By addressing geriatric\u2011specific vulnerabilities through a facilitated digital interface, this ML\u2011driven approach complements traditional clinical consultations. These findings support the scalable integration of artificial intelligence\u2013assisted decision support to foster patient\u2011centered care in aging populations.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/83238","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:27:25Z","timestamp":1776104845000},"page":"e83238-e83238","source":"Crossref","is-referenced-by-count":0,"title":["Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4359-7401","authenticated-orcid":false,"given":"Yi-Ting","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0872-8664","authenticated-orcid":false,"given":"Yen Chun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7665-5325","authenticated-orcid":false,"given":"Chih Kuang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9786-5371","authenticated-orcid":false,"given":"Hsiao-Yun","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8278-0033","authenticated-orcid":false,"given":"Mingchih","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.pec.2005.06.010","article-title":"An integrative model of shared decision making in medical encounters","volume":"60","author":"Makoul","journal-title":"Patient Educ Couns"},{"issue":"9","key":"R2","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1056\/NEJMp1109283","article-title":"Shared decision making: pinnacle of patient-centered care","volume":"366","author":"Barry","journal-title":"N Engl J Med"},{"issue":"6","key":"R3","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1038\/s41585-023-00840-0","article-title":"Shared decision-making before prostate cancer screening decisions","volume":"21","author":"Pekala","journal-title":"Nat Rev Urol"},{"issue":"5","key":"R4","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/s0277-9536(96)00221-3","article-title":"Shared decision-making in the medical encounter: what does it mean? 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