Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC
The I 3 LUNG study enrolled 2,396 patients in a real‑world, multimodal AI trial, integrating clinical and blood data, CT images, digital pathology, and genomics into machine‑learning early fusion (MLEF) and deep‑learning intermediate fusion (DLIF) models. Machine‑learning CB‑only models achieved AUCs up to 0.77 in the test set, outperforming traditional markers such as PD‑L1, ECOG PS, NLR, LDH, and LIPI, while external validation showed a performance drop (AUC 0.55–0.72). A clinical usability study demonstrated that both expert and non‑expert physicians improved their predictions when using t…
The I 3 LUNG study enrolled 2,396 patients in a real‑world, multimodal AI trial, integrating clinical and blood data, CT images, digital pathology, and genomics into machine‑learning early fusion (MLEF) and deep‑learning intermediate fusion (DLIF) models. Machine‑learning CB‑only models achieved AUCs up to 0.77 in the test set, outperforming traditional markers such as PD‑L1, ECOG PS, NLR, LDH, and LIPI, while external validation showed a performance drop (AUC 0.55–0.72). A clinical usability study demonstrated that both expert and non‑expert physicians improved their predictions when using t…
Swiss AI researchers can draw on this large international real‑world study to assess the clinical impact and explainability of AI decision support systems in oncology.
Original source record. Open the original record to verify the underlying announcement.