A recent study published in JAMA Network Open highlights the potential of a machine-learning algorithm to assess tumor-infiltrating lymphocytes in melanoma, addressing the limitations of traditional, subjective pathologist evaluations. The findings suggest that AI can provide a more objective and reproducible assessment of this vital prognostic biomarker. David Rimm, MD, PhD, from Yale University, discussed the future roles of AI in oncology, including genomic analysis and clinical decision support tools. He noted that AI could streamline pathologists’ workflows by automating the identification of normal tissues, thereby enhancing efficiency and accuracy. While some AI applications, such as determining BRAF mutations, show promise, others may still fall short in sensitivity. Rimm emphasized that as AI continues to evolve, its utility in clinical settings will expand, including applications in medicine and radiology for rapid literature review and evidence-based practices. This evolution is poised to transform cancer research and patient care dramatically.
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