AI-Augmented Diagnostic Assessment With ENLIGHT Versus Independent Pathologist Review

This study will evaluate whether artificial intelligence (AI) can enhance clinicians' accuracy, efficiency, and confidence in distinguishing lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC) and kidney renal papillary cell carcinoma (KIRP) from kidney renal clear cell carcinoma (KIRC) using digitized pathology slides. These subtype classifications are routinely performed by pathologists but can be challenging and time-consuming, particularly in difficult cases. During the study, participating clinicians will review lung and kidney pathology slides under three different conditions: * Unaided Review: Diagnosis without AI assistance. * AI as Double-Check: The clinician first makes an independent diagnosis, after which the AI-generated diagnosis (prediction only or prediction with explanation) is revealed for review. * AI as First-Look: The AI-generated diagnosis (prediction only or prediction with explanation) is presented before the clinician begins the review. Clinicians will be randomly assigned to different review sequences to minimize potential order effects. This study design will enable us to assess the impact of AI assistance on diagnostic accuracy, interpretation time, and clinician confidence.

Inclusion Criteria for Pathology Slides (i.e., Cases): * Hematoxylin and eosin (H\&E)-stained pathology slides * Final diagnosis confirmed through molecular testing in conjunction with expert pathology evaluation Exclusion Criteria for Pathology Slides (i.e., Cases): * Poor-quality or unreadable slides * Cases used in AI training Inclusion Criteria for Readers (i.e., Participants): * Board-certified or board-eligible pathologists * Willingness to complete both unaided and AI-assisted review sessions