Perception-Aligned AI Outputs: End-to-End Visual Prediction for Uncertainty Communication in Clinical Decision-Making
Harvard Ophthalmology AI Lab（哈佛眼科人工智能实验室）；Schepens Eye Research Institute of Massachusetts Eye and Ear（马萨诸塞眼耳医院施佩恩眼科研究所）；Harvard Medical School（哈佛医学院）；Center for Advanced Technology and Education（先进教育技术中心）；Florida International University（佛罗里达国际大学）；Dartmouth Institute for Health Policy and Clinical Practice（达特茅斯健康政策与临床实践研究所）；Dartmouth College（达特茅斯学院）；Computer Aided Medical Procedures（医学辅助程序）；Technical University of Munich（慕尼黑技术大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；Department of Ophthalmology, Byers Eye Institute, Stanford University（眼科部门，比尔斯眼科研究所，斯坦福大学）
Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians and patients to interpret. We introduce Visualized Learning for Machine Learning (VL4ML), a human-centered explainability framework that communicates model predictions and uncertainty through intuitive visual representations rather than numerical or post-hoc explanations. By encoding diagnostic information in colors, patterns, and spatial structures, VL4ML enables users to interpret predictions without requiring knowledge of model internals or statistical expertise. We demonstrate the framework across multiple clinical tasks, including classification, regression, longitudinal prediction, and multimodal analysis. Its effectiveness was evaluated through a human-centered study involving 158 participants (39.2% clinical professionals) and an expert interpretability assessment. More than 79% of participants positively rated the visual explanations across evaluation dimensions, 84.0% found them more memorable than numeric outputs, and 76.9% reported faster decision-making. Over 82% successfully perceived uncertainty embedded in the visual representations without prior statistical training. No significant differences were observed between clinicians and non-clinicians or between male and female participants, indicating broad accessibility. These results suggest that VL4ML complements existing XAI and uncertainty quantification methods by providing intuitive, universally interpretable visual explanations that support transparent and trustworthy clinical decision-making.
Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment
Real-time Reconstruction of Human Visual Perception from fMRI
Princeton University（普林斯顿大学）；Dartmouth College（达特茅斯学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Harvard University（哈佛大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Medical AI Research Center (MedARC)（医学人工智能研究中心）
NeuroVoxel-LM: Language-Aligned 3D Perception via Dynamic Voxelization and Meta-Embedding
School of Electrical and Electronic Engineering（电气与电子工程学院）；Nanyang Technological University（南洋理工大学）；School of Computer Science and Technology（计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）
TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Rochester（罗切斯特大学）
From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction
Technological University Dublin（都柏林理工大学）；Aalborg University（奥尔堡大学）；Health Management Institute（健康管理研究所）；Trinity College Dublin（都柏林圣三一学院）
Clinically Aligned Geometry Constraints for Robust IVUS Vessel Boundary Segmentation
AIM For Health Lab, Monash University（莫纳什大学AIM健康实验室）；Department of Data Science and Artificial Intelligence, Faculty of IT, Monash University（莫纳什大学信息技术学院数据科学与人工智能系）；Monash University Victorian Heart Institute（莫纳什大学维多利亚心脏研究所）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）；National Cerebral and Cardiovascular Center（国立循环器病研究中心）；Department of Cardiology, Chonnam National University Hospital and Medical School（全南大学医院和医学院心脏病学系）
