A Learning Program for Treatment Recommendations by Molecular Tumor Boards and Artificial Intelligence.

ABSTRACT:

Importance

Substantial heterogeneity exists in treatment recommendations across molecular tumor boards (MTBs), especially for biomarkers with low evidence levels; therefore, the learning program is essential.

Objective

To determine whether a learning program sharing treatment recommendations for biomarkers with low evidence levels contributes to the standardization of MTBs and to investigate the efficacy of an artificial intelligence (AI)-based annotation system.

Design, setting, and participants

This prospective quality improvement study used 50 simulated cases to assess concordance of treatment recommendations between a central committee and participants. Forty-seven participants applied from April 7 to May 13, 2021. Fifty simulated cases were randomly divided into

SUBMITTER: Sunami K 

PROVIDER: S-EPMC10690580 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature