This Topical Collection seeks to establish rigorous technical standards for computational models across all domains of medical research and clinical practice. While previous work in medical diagnostics has highlighted both the potential of high-dimensional imaging (CT, MRI, Pathology) alongside low-dimensional clinical factors (genomics, EHR, lab values), these approaches often lead to computationally non-trivial models where over-parameterisation creates “source bias” with pixel-dense data overpowering sparse clinical indicators.

Why is this topic important?

Computational models are increasingly central to modern medicine, spanning diagnostic interpretation, treatment planning, outcome prediction, and therapeutic optimization. However, inconsistent methodological approaches across these domains create barriers to clinical translation, reproducibility, and equitable application. Establishing unified technical standards will accelerate the development of reliable, interpretable models that can be safely deployed across diverse clinical contexts.

What is the aim of the Methods Collection/how will this Methods Collection help the research community?

We propose rigorous technical protocols for developing and validating multimodal computational models in medicine, applicable to diagnostics, treatment planning, prognostic modelling, and therapeutic decision support. These protocols will address equitable data fusion strategies, such as vector grounding schemes, tensor fusion, and attention mechanisms, aiming to prevent any single modality from dominating model decisions. By focusing on fusion methodology rather than specific pathologies or clinical applications, this collection provides a cross-disciplinary toolkit for researchers in oncology, neurology, cardiology and other medical specialities to build robust, interpretable, and clinically actionable computational models.