Abstract
Biomedical research requires repeated judgments about what heterogeneous evidence supports. This Perspective proposes a sequential workflow in which quantitative bioinformatics identifies candidates, semantic decision models organize and prioritize evidence, and large language models provide subsequent analysis before human experts interpret the findings and make the final decisions. Jev and Laya illustrate bounded question-answering interfaces that could help concentrate more costly analysis on relevant candidates. Across molecular, cellular, tissue, individual and population scales, this workflow offers a practical path to focus expert attention on the evidence and candidates most likely to matter. An accompanying question catalogue and AI assistant skill make the approach ready for prospective evaluation. By combining computational efficiency with expert judgment, semantic decision models could help biomedical researchers explore more evidence, ask sharper questions and make better-informed decisions.