{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:49:22Z","timestamp":1791560962851,"version":"4.3.4"},"reference-count":18,"publisher":"Ovid Technologies (Wolters Kluwer Health)","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"\n Objective<\/jats:title>\n To conduct a pilot study to assess whether saliency-based explainable artificial intelligence (XAI) affects the recognition of genetic conditions from facial images.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Forty-four medical geneticists, divided into AI-only and XAI-supported groups, assessed 18 images of individuals with or without genetic conditions. Diagnostic accuracy and confidence were recorded before and after viewing an AI classifier\u2019s prediction probability, with or without XAI explanations. Mediation analyses were conducted to better interpret how geneticists interact with AI and XAI in decision-making.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n AI-only and XAI support improved accuracy for correct AI classifications, while incorrect AI classifications decreased accuracy. Average confidence increased with correct and decreased with incorrect classification. Geneticists reported that AI prediction probability was useful, whereas XAI explanations were viewed less favorably. For incorrect AI classifications, there was a negative correlation between accuracy improvement and perceived AI usefulness. When AI was correct (without XAI), the model prediction probability acted as a mediator between user confidence and the user\u2019s decision to choose the same answer as AI.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The lack of accuracy or confidence improvements indicates that participants did not integrate saliency-based XAI into decisions. AI prediction probability had a greater impact on participants\u2019 decision-making. These data may help inform larger studies.<\/jats:p>\n <\/jats:sec>","DOI":"10.1097\/mcd.0000000000000581","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:00:12Z","timestamp":1791558012000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Application of deep learning and explainable AI-supported medical decision-making for facial phenotyping in genetic syndromes"],"prefix":"10.1097","author":[{"given":"\u00d6mer","family":"S\u00fcmer","sequence":"first","affiliation":[{"name":"Human-Centered Artificial Intelligence, University of Augsburg, Faculty of Applied Computer Science, Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Huber","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt, Ingolstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dat","family":"Duong","sequence":"additional","affiliation":[{"name":"Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanna E.","family":"Ledgister Hanchard","sequence":"additional","affiliation":[{"name":"Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Conati","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Andr\u00e9","sequence":"additional","affiliation":[{"name":"Human-Centered Artificial Intelligence, University of Augsburg, Faculty of Applied Computer Science, Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin D.","family":"Solomon","sequence":"additional","affiliation":[{"name":"Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebekah L.","family":"Waikel","sequence":"additional","affiliation":[{"name":"Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"R1-20261009","article-title":"Towards better understanding of gradient-based attribution methods for deep neural networks.","author":"Ancona","year":"2017","journal-title":"arXiv preprint arXiv"},{"key":"R2-20261009","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1111\/cge.12716","article-title":"Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis.","volume":"89","author":"Basel\u2010Vanagaite","year":"2016","journal-title":"Clin Genet"},{"key":"R3-20261009","doi-asserted-by":"crossref","first-page":"990604","DOI":"10.3389\/fmed.2022.990604","article-title":"Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey.","volume":"9","author":"Chen","year":"2022","journal-title":"Front Med (Lausanne)"},{"key":"R4-20261009","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1093\/jamia\/ocz229","article-title":"Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.","volume":"27","author":"Diprose","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"R5-20261009","doi-asserted-by":"crossref","first-page":"864092","DOI":"10.3389\/fgene.2022.864092","article-title":"Neural networks for classification and image generation of aging in genetic syndromes.","volume":"13","author":"Duong","year":"2022","journal-title":"Front Genet"},{"key":"R6-20261009","first-page":"100053","article-title":"Neural network classifiers for images of genetic conditions with cutaneous manifestations.","volume":"3","author":"Duong","year":"2022","journal-title":"HGG Adv"},{"key":"R8-20261009","doi-asserted-by":"crossref","first-page":"e1011168","DOI":"10.1371\/journal.pgen.1011168","article-title":"Comparison of clinical geneticist and computer visual attention in assessing genetic conditions.","volume":"20","author":"Duong","year":"2024","journal-title":"PLoS Genet"},{"key":"R9-20261009","doi-asserted-by":"crossref","first-page":"e2440969","DOI":"10.1001\/jamanetworkopen.2024.40969","article-title":"Large language model influence on diagnostic reasoning: a randomized clinical trial.","volume":"7","author":"Goh","year":"2024","journal-title":"JAMA Netw Open"},{"key":"R10-20261009","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1038\/s41588-021-01010-x","article-title":"GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.","volume":"54","author":"Hsieh","year":"2022","journal-title":"Nat Genet"},{"key":"R11-20261009","doi-asserted-by":"crossref","first-page":"103571","DOI":"10.1016\/j.artint.2021.103571","article-title":"Local and global explanations of agent behavior: integrating strategy summaries with saliency maps.","volume":"301","author":"Huber","year":"2021","journal-title":"Artif Intell"},{"key":"R14-20261009","first-page":"299","article-title":"Using clinical simulation to evaluate AI-enabled decision support.","volume":"310","author":"Lyell","year":"2024","journal-title":"Stud Health Technol Inform"},{"key":"R15-20261009","doi-asserted-by":"crossref","first-page":"825565","DOI":"10.3389\/frai.2022.825565","article-title":"Ganterfactual\u2014counterfactual explanations for medical non-experts using generative adversarial learning.","volume":"5","author":"Mertes","year":"2022","journal-title":"Front Artif Intell"},{"key":"R16-20261009","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s00247-023-05789-1","article-title":"Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.","volume":"54","author":"Rassmann","year":"2024","journal-title":"Pediatr Radiol"},{"key":"R17-20261009","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1038\/s42256-022-00536-x","article-title":"Benchmarking saliency methods for chest X-ray interpretation.","volume":"4","author":"Saporta","year":"2022","journal-title":"Nat Mach Intell"},{"key":"R19-20261009","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1038\/s41562-024-02024-1","article-title":"When combinations of humans and AI are useful: a systematic review and meta-analysis.","volume":"8","author":"Vaccaro","year":"2024","journal-title":"Nat Hum Behav"},{"key":"R20-20261009","doi-asserted-by":"crossref","first-page":"e211276","DOI":"10.1001\/jamanetworkopen.2021.1276","article-title":"Association of clinician diagnostic performance with machine learning-based decision support systems: a systematic review.","volume":"4","author":"Vasey","year":"2021","journal-title":"JAMA Netw Open"},{"key":"R21-20261009","doi-asserted-by":"crossref","first-page":"e242609","DOI":"10.1001\/jamanetworkopen.2024.2609","article-title":"Recognition of genetic conditions after learning with images created using generative artificial intelligence.","volume":"7","author":"Waikel","year":"2024","journal-title":"JAMA Netw Open"},{"key":"R22-20261009","article-title":"Fast treeshap: accelerating shap value computation for trees.","author":"Yang","year":"2021","journal-title":"arXiv preprint arXiv"}],"container-title":["Clinical Dysmorphology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.1097\/MCD.0000000000000581","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:00:15Z","timestamp":1791558015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.1097\/MCD.0000000000000581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,8]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1097\/mcd.0000000000000581","relation":{},"ISSN":["0962-8827","1473-5717"],"issn-type":[{"value":"0962-8827","type":"print"},{"value":"1473-5717","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,8]]},"assertion":[{"value":"2026-03-23","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-08-16","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}