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Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence xAI to introduce AI-derived AID markers for clinical decision support. We used xAI to decode the outcome of 15, patients across 38 solid cancer entities based on markers, including clinical records, image-derived body compositions, and mutational tumor profiles.
Moreover, xAI enabled us to uncover 1, prognostic interactions between markers. Our approach was validated in an independent cohort of 3, patients with lung cancer from a US nationwide electronic health record-derived database. These results show the potential of xAI to transform the assessment of clinical variables and enable personalized, data-driven cancer care. Despite the vast amount of multimodal clinical data currently available for each patient in modern healthcare, the promise of personalized medicine has yet to be realized.
A promising strategy to overcome this limitation is to integrate clinical data from multiple sources, such as medical history, laboratory test results, imaging data and omics analyses 1 , 4. Advances in machine learning and the increasing availability of digitally accessible data made it possible to model complex relationships between prognostic markers on a large scale 1 , 5 , 6 , 7 , 8 , 9.
Together with recent methods for understanding the decision-making of such models, referred to as explainable artificial intelligence xAI , this makes it possible to assess individual patient prognosis and unravel the contribution of each variable 10 , 11 , 12 , 13 , 14 , In this study, we leveraged these advances by proposing an approach for decoding prognostic hallmarks based on large-scale real-world data RWD.
