Differentiable Neural Architecture Search for medical image segmentation: A systematic review and field audit

Medical image segmentation is critical for diagnosis, treatment planning, and disease monitoring, yet differs from generic semantic segmentation due to volumetric data, modality-specific artifacts, costly and uncertain expert annotations, and domain shift across scanners and institutions. Neural Architecture Search (NAS) can automate model design, but many NAS paradigms become impractical for 3D segmentation because evaluating large numbers of candidate architectures is computationally prohibitive. Differentiable NAS (DNAS) alleviates this barrier by optimizing relaxed architectural choices with gradients in a weight-sharing supernet, making search feasible under realistic compute and memory budgets. However, DNAS introduces distinct methodological risks (e.g., optimization instability and discretization gap) and raises challenges in reproducibility and clinical deployability. We conduct a PRISMA-inspired systematic review of DNAS for medical image segmentation (multi-database screening, 2018-2025), retaining 33 papers representing 31 unique methods for quantitative analysis. Across the included studies, external validation on independent-site data is rare (∼10%), full code release (including search procedures) is limited (∼26%), and only a minority substantively addresses search stability (∼23%). Despite clear clinical relevance, multi-objective search that explicitly optimizes latency or memory is also uncommon (∼23%). We position DNAS within the broader NAS landscape, introduce a segmentation-focused taxonomy, and propose a NAS Reporting Card tailored to medical segmentation to improve transparency, comparability, and reproducibility.