npj Digital Medicine, Published online: 23 October 2025; doi:10.1038/s41746-025-02003-4
HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings]]>npj Digital Medicine, Published online: 21 October 2025; doi:10.1038/s41746-025-02082-3
Although still at a nascent state, quantum computing promises advances in healthcare, from drug discovery to personalised treatments. But it also threatens current cryptographic systems that protect medical data and infrastructure. The concept of âQ-Dayâ highlights risks such as âharvest now, decrypt laterâ attacks, with particular concerns for medical devices and sensitive applications in fields like femtech. Preparing for this future requires the rapid adoption of post-quantum cryptography, the coordination of time-phased and scalable âtechnology rolloutâ strategies, and revised regulatory frameworks to safeguard patient safety, privacy, and trust.]]>npj Digital Medicine, Published online: 21 October 2025; doi:10.1038/s41746-025-02078-z
Streamlined machine learning model for early sepsis risk prediction in burn patients]]>npj Digital Medicine, Published online: 20 October 2025; doi:10.1038/s41746-025-01992-6
Unlocking the potential: multimodal AI in biotechnology and digital medicineâeconomic impact and ethical challenges]]>npj Digital Medicine, Published online: 20 October 2025; doi:10.1038/s41746-025-01988-2
A deep learning based automatic report generator for retinal optical coherence tomography images]]>npj Digital Medicine, Published online: 17 October 2025; doi:10.1038/s41746-025-02008-z
When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior]]>npj Digital Medicine, Published online: 17 October 2025; doi:10.1038/s41746-025-01989-1
Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models]]>npj Digital Medicine, Published online: 17 October 2025; doi:10.1038/s41746-025-01987-3
Navigating the tradeoff between personal privacy and data utility in speech anonymization for clinical research]]>