Prospective Studies on Artificial Intelligence for Cancer Pathology Evaluation
Prospective Studies on Artificial Intelligence for Cancer Pathology Evaluation
This study will test whether artificial intelligence (AI) can help doctors diagnose a rare blood cancer called acute promyelocytic leukemia (APL) more quickly and accurately. Doctors usually examine bone marrow samples under a microscope to make this diagnosis, but it can be challenging and time-consuming.
In this study, doctors will review bone marrow samples under three different conditions:
Doctors will be randomly assigned to different orders of these three conditions. This design will allow us to compare how AI support affects diagnostic accuracy, speed, and confidence.
This study aims to evaluate the effect of artificial intelligence (AI) assistance on clinicians' diagnostic performance in detecting acute promyelocytic leukemia (APL) using Wright-Giemsa-stained bone marrow whole-slide images (WSIs). The Leukemia End-to-End Analysis Platform (LEAP) will serve as the AI model under assessment.
This is a single-session, within-reader study. Participants will be randomly assigned to one of two study arms, which differ in the order of diagnostic blocks:
* Arm 1 (X -> Y): Block X (Unaided Review): Clinicians review WSIs without AI support. Diagnostic accuracy, time to decision, and confidence will be recorded.
Block Y (AI-Assisted Review): Comprising two sub-blocks presented in randomized order:
Y1 (AI as Double-Check): Clinicians provide an initial diagnosis and confidence score without the aid of AI. AI predictions are then revealed, and clinicians may revise their diagnosis. Both pre-AI and post-AI decisions will be recorded.
Y2 (AI as First Look): Clinicians review WSIs with AI-predicted diagnoses visible from the beginning.
* Arm 2 (Y -> X): Block Y (AI-Assisted Review): Sub-blocks Y1 and Y2 presented in randomized order.
Block X (Unaided Review): As described above.
Each clinician will review 102 de-identified WSIs. For each reader, slides will be randomly divided into three disjoint subsets (e.g. 34/34/34), stratified by APL status, and assigned to Block X (Unaided), Block Y1 (AI as Double-Check), or Block Y2 (AI as First Look). No slide will be shown to the same reader in more than one block.
In addition, the AI system will independently generate diagnostic predictions for all WSIs to enable benchmarking; however, this does not constitute a participant arm.
Ground-truth diagnoses will be determined by molecular confirmation and expert consensus.
Inclusion Criteria for Pathology Slides (i.e., Cases):
Exclusion Criteria for Pathology Slides (i.e., Cases):
Inclusion Criteria for Readers (i.e., Participants):