Enhancing Trust in Clinical AI: Adaptive Testing of KOA Diagnostic Models with SmartTest-KOA

Enhancing Trust in Clinical AI: Adaptive Testing of KOA Diagnostic Models with SmartTest-KOA | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 July 2025 V1 Latest version Share on Enhancing Trust in Clinical AI: Adaptive Testing of KOA Diagnostic Models with SmartTest-KOA Authors : Kanthavel R , Adline Freeda [email protected] , and Dhaya R Authors Info & Affiliations https://doi.org/10.22541/au.175310001.10095919/v1 180 views 83 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To ensure clinical efficacy and patient safety, it is crucial to develop trustworthy and reliable artificial intelligence (AI) systems for diagnosing knee osteoarthritis (KOA). Given that the traditional static testing strategy is insufficient to validate AI systems in the dynamic and practical reality of clinical settings, we have developed SmartTest-KOA. This novel framework applies reinforcement learning (RL) principles to continuously and adaptively test AI-based KOA diagnostic software. The SmartTest-KOA framework autonomously generates targeted test cases, engages in real-time monitoring of model behaviour, and enables re-training of the model using failure-raising inputs. SmartTest-KOA conducts tests using domain-specific metamorphic relations (MR) and code coverage to help identify hidden variants and improve software reliability. SmartTest-KOA was validated using convolutional neural networks (CNN) with KOA imaging datasets that had been pre-trained on Open Images, as well as example medical datasets identified in the relevant literature. SmartTest-KOA improved code coverage from 47% to 80%, and revealed latent vulnerabilities that threatened the model’s accuracy. SmartTest-KOA advances a regulatory-compliant, algorithm-agnostic, and scalable solution for ensuring the lifecycle of AI software deployed in clinical settings, leveraging inherent pathways in FDA and EU use case regulations. Supplementary Material File (1.smarttest +koa-1.docx) Download 1.97 MB Information & Authors Information Version history V1 Version 1 21 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adaptive testing framework ai-driven medical imaging continuous software validation knee osteoarthritis diagnosis (koa) reinforcement learning (rl) software regression detection Authors Affiliations Kanthavel R Papua New Guinea University of Technology View all articles by this author Adline Freeda [email protected] KCG College of Technology Department of Information Technology View all articles by this author Dhaya R Papua New Guinea University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 180 views 83 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Kanthavel R, Adline Freeda, Dhaya R. Enhancing Trust in Clinical AI: Adaptive Testing of KOA Diagnostic Models with SmartTest-KOA. 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