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npj Digital Medicine

Publisher:
Springer Nature
ISSN:
2398-6352
Category:
MEDICAL INFORMATICS
Impact factor:
12.4
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158 parsed articles

Last update: 2026-10-23

Latest articles

A deep learning framework for automated cervical spinal cord vertebral labeling and morphometric extraction from brain MRI with large-scale multicenter validation

2026-10-23

Gustavo M. Jarola, Diogo H. Shiraishi, Isaac M. Adanyeguh, Tetsuo Ashizawa, Orlando Graziani Povoas Barsottini, Benjamin Bender, Fernando Cendes, Louise A. Corben, Manuela Corti, Léo Coutinho, Emile d’Angremont, Alfredo Damasceno, Martin B. Delatycki, Imis Dogan, Alexandra Durr, Gary F. Egan, Jennifer Faber, Juan Fernandez-Ruiz, Judith van Gaalen, William Gaetz, Nellie Georgiou-Karistianis, Eva van Heese, Pierre-Gilles Henry, Carlos R. Hernandez-Castillo, Diane Hutter, James M. Joers, Thomas Klockgether, Max Laansma, Christophe Lenglet, Jiakun Li, Eric F. Lock, Raffaele Lodi, David R. Lynch, David N. Manners, Alberto RM Martinez, Sergio Eiji Ono, Gülin Öz, Massimo Pandolfo, José Luiz Pedroso, Myriam Rai, Kathrin Reetz, Timothy P. Roberts, Sandro Romanzetti, David A. Rudko, Susmita Saha, Ludger Schoels, Sub H. Subramony, Zhuopin Sun, Matthis Synofzik, Hélio A. Ghizoni Teive, Gabriel de Deus Vieira, Bart van de Warrenburg, Ysbrand van der Werf, Marcondes C. França, Ian H. Harding, Thiago J. R. Rezende

DOI: 10.1038/s41746-026-03403-w

Meaningful oversight of medical AI beyond human in the loop

2026-07-23

Davy van de Sande

npj Digital Medicine, Published online: 23 July 2026; doi:10.1038/s41746-026-02971-1 Human oversight of medical AI is increasingly required, but clinician presence alone does not make oversight meaningful. We propose four interlocking conditions—epistemic capacity, cognitive space, decisional authority, and intervention effectiveness—that determine whether human judgment can function as a safety mechanism. Embedding these conditions across procurement, deployment, monitoring, and decommissioning can turn oversight into an operational patient-safety capability across predictive, generative, semi-autonomous, and agentic systems.]]>

DOI: 10.1038/s41746-026-02971-1

Can Laws Be Flexible? Rethinking Legislation for Innovation

2026-07-22

Nick K. Schneider

npj Digital Medicine, Published online: 22 July 2026; doi:10.1038/s41746-026-02846-5 Agile legislation adapts principles from agile software development to lawmaking, emphasizing iteration, multi-stakeholder feedback, and embedded revision. We outline this learning-oriented governance model using three case studies: Germany’s stepwise digital health legislation, the EU AI Act, and U.S. FDA user-fee reauthorization. These examples highlight legislative designs that enable structured generation of real-world data and evidence during implementation, informing regulatory interpretation and iterative refinement in rapidly evolving technological domains.]]>

DOI: 10.1038/s41746-026-02846-5

Handling missing data: AI approach for survival prediction in lung cancer despite missing data

2026-07-16

Margaret Y. Sui

npj Digital Medicine, Published online: 16 July 2026; doi:10.1038/s41746-026-03019-0 Optimal cancer prognostication combines multimodal data including biopsy results, CT scan, patient characteristics, and clinical trajectory thus far. However, many patients do not have all modalities of data available, so requiring physicians to have all patient data modalities to use an AI prediction algorithm limits the tool’s clinical utility. To increase the clinical potential of these AI algorithms, Ruffini et al. developed a “missing data aware” survival prediction approach that is able to handle inputs with missing data for risk stratification in non-small cell lung cancer patients.]]>

DOI: 10.1038/s41746-026-03019-0

Embedded transparency in artificial intelligence: a prerequisite for equity and representation in AI-enabled clinical trials

2026-07-11

Johanna M. C. Blom

npj Digital Medicine, Published online: 11 July 2026; doi:10.1038/s41746-026-02987-7 Artificial intelligence is being embedded in clinical trial infrastructure, shaping who is identified, stratified, and analysed. Opaque models risk amplifying existing disparities in the evidence base. We argue that embedded transparency, the structural integration of ex ante interpretability, demographic auditability, documented uncertainty handling, and stakeholder-relative explanation, is a necessary, though not sufficient, condition for equitable AI-enabled trials, and propose governance recommendations actionable across regulatory regimes.]]>

DOI: 10.1038/s41746-026-02987-7