AI-Assisted Acute Myeloid Leukemia Evaluation With the Leukemia End-to-End Analysis Platform (LEAP) Versus Clinician-Only Assessment

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):

• Wright-Giemsa-stained bone marrow aspirate smears
• 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, or board-certified/board-eligible hematologists who routinely make hematopathology diagnoses in their clinical practice
• Willingness to complete both unaided and AI-assisted review sessions