Chouvarda Ioanna, Colantonio Sara, Verde Ana S C, Jimenez-Pastor Ana, Cerdá-Alberich Leonor, Metz Yannick, Zacharias Lithin, Nabhani-Gebara Shereen, Bobowicz Maciej, Tsakou Gianna, Lekadir Karim, Tsiknakis Manolis, Martí-Bonmati Luis, Papanikolaou Nikolaos
School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Institute of Information Science and Technologies of the National Research Council of Italy, Pisa, Italy.
Eur Radiol Exp. 2025 Jan 15;9(1):7. doi: 10.1186/s41747-024-00543-0.
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging. A total of 49 responses were obtained and analysed to identify trends and patterns. While TGs valued transparency and traceability the most, CGs pointed out the importance of explainability. Among the topics where TGs may benefit from further exposure are stability and robustness checks, and mitigation of fairness issues. On the other hand, CGs seemed more reluctant towards synthetic data for validation and would benefit from exposure to cross-validation techniques, or segmentation metrics. Topics emerging from the open questions were utility, capability, adoption and trustworthiness. These findings on current trends in AI validation strategies may guide the creation of guidelines necessary for training the next generation of professionals working with AI in healthcare and contribute to bridging any technical-clinical gap in AI validation. RELEVANCE STATEMENT: This study recognised current gaps in understanding and applying AI validation strategies in cancer imaging and helped promote trust and adoption for interdisciplinary teams of technical and clinical researchers. KEY POINTS: Clinical and technical researchers emphasise interpretability, external validation with diverse data, and bias awareness in AI validation for cancer imaging. In cancer imaging AI research, clinical researchers prioritise explainability, while technical researchers focus on transparency and traceability, and see potential in synthetic datasets. Researchers advocate for greater homogenisation of AI validation practices in cancer imaging.
人工智能（AI）模型验证的良好实践是实现可信AI的关键。在癌症成像领域，这项工作吸引了临床和技术AI爱好者的关注，讨论了AI验证策略中当前存在的差距，研究了技术组（TGs）和临床组（CGs）中常见或不同的现有实践。这项工作基于一系列涵盖多个AI验证主题的结构化问题，这些问题是针对从事医学成像AI工作的专业人员提出的。共获得并分析了49份回复，以识别趋势和模式。虽然TGs最重视透明度和可追溯性，但CGs指出了可解释性的重要性。TGs可能从进一步了解中受益的主题包括稳定性和鲁棒性检查以及公平性问题的缓解。另一方面，CGs似乎对用于验证的合成数据更为抵触，接触交叉验证技术或分割指标会对他们有益。开放性问题中出现的主题包括实用性、能力、采用和可信度。这些关于AI验证策略当前趋势的发现可能会指导制定培训下一代医疗保健领域AI专业人员所需的指南，并有助于弥合AI验证中任何技术与临床之间的差距。相关性声明：本研究认识到当前在癌症成像中理解和应用AI验证策略方面存在的差距，并有助于促进技术和临床研究人员跨学科团队的信任和采用。关键点：临床和技术研究人员强调癌症成像AI验证中的可解释性、使用多样化数据进行外部验证以及偏差意识。在癌症成像AI研究中，临床研究人员优先考虑可解释性，而技术研究人员则专注于透明度和可追溯性，并看到合成数据集的潜力。研究人员主张在癌症成像中使AI验证实践更加同质化。
