Integrating artificial intelligence with human reasoning in oncology: questions on real-world implementation and patient-centric evidence

Abstract

Artificial intelligence (AI), Precision medicine, Standard of careThe article by Jiang et al. [1], "Leveraging artificial intelligence for clinical decision support in personalized standard regimen recommendation for cancer" published in Military Medical Research, addresses a pivotal issue in contemporary oncology: how artificial intelligence (AI) can augment clinical reasoning to refine regimen selection.Their discussion of data learnability, model usability, and the envisioned SINGULARITY framework reflects a forward-looking approach to precision medicine.The integration of real-world evidence into multimodal AI is indeed a necessary evolution toward context-aware decision support systems.Nevertheless, several important questions emerge from their proposal that may further enrich the dialogue on AI-guided oncology.While the authors underscore the limitations of static and cross-sectional data in existing AI models, the question remains how temporal dynamics, such as treatment response trajectories, clonal evolution, or changing comorbidities, will be represented.Longitudinal modeling requires harmonized, repeated measures across diverse modalities, yet electronic health records and omic repositories are often incomplete or asynchronous [2].How might multimodal systems reconcile these temporal mismatches without introducing bias or losing clinical interpretability?Moreover, in adaptive oncology, where therapy sequences are continually adjusted, can AI truly mirror the nuanced reasoning by which clinicians weigh prior outcomes, toxicity, and patient tolerance?The SINGULARITY study proposes the use of "realworld data adhering to rigorous standards of clinical trials".This hybrid approach raises methodological questions.Real-world data, by definition, lack the controlled assignment and homogeneity of trial settings [3].How will the study account for confounders, missing data, and variations in data provenance when constructing the AI model?Will causal inference frameworks, such as target trial emulation or inverse probability weighting, be integrated to preserve validity while leveraging observational inputs?Without such safeguards, there is a risk that the abundant real-world heterogeneity could compromise causal clarity and reproducibility.Equally compelling is the issue of explainability.The authors note that AI should act as an adjunct to clinicians, offering reasoning beyond human perception.Yet as models become more complex, interpretability often declines.How will the SINGULARITY system ensure that its recommendations are transparent enough to be trusted, contested, or refined by oncologists?Explainable AI methods, such as attention maps, feature attribution,

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