This paper introduces the FHIR Resource Access Graph (FRAG) to formally model and detect concurrency-related race conditions—such as Simultaneous Write Conflict and TOCTOU Authorization Violation—in h…

The paper proposes and validates a comprehensive four-layer Zero Trust security architecture designed to mitigate critical vulnerabilities in autonomous AI agents handling Protected Health Information…

This paper introduces KliniskVestBERT, a suite of BERT models specialized by pre-training on a large, diverse corpus of real-world Norwegian clinical texts, demonstrating superior performance for clin…

The paper introduces ClinEnv, a novel interactive, multi-stage benchmark designed to evaluate LLMs' decision-making and information-gathering process during longitudinal inpatient medical simulations.

This paper conducts the first comparative study of Differential Privacy (DP), Named Entity Recognition (NER), and Large Language Models (LLMs) for de-identifying Dutch clinical notes, finding that com…

The paper proposes a Sovereign AI architecture for clinical triage that ensures maximum security by performing all inference on-device and receiving data only through physically unidirectional channel…

This paper analyzes the intersection of clinical handling practices and cyberbiosecurity risks associated with both regulated and unregulated (DIY) artificial pancreas systems, highlighting the legal…

This paper proposes SynPre-FL, a framework that combines high-fidelity synthetic EHR generation with federated learning for robust clinical prediction.

The paper proposes a comprehensive, phased hybrid migration framework to transition vulnerable IoT-based healthcare systems to quantum-safe cryptography.

The paper introduces MedCase-Structured, a synthetic, FHIR-formatted dataset designed to benchmark diagnostic reasoning in realistic EHR settings, showing that LLMs perform worse on structured data th…

The paper models healthcare mechanism design as program synthesis, demonstrating that an optimized, mixed-objective program can eliminate up-coding and reduce patient rejection while maintaining finan…

A LLM powered system is presented for generating formally verified code for structured data translation between JSON schema of a pulse oximeter and FHIR format in Medical IoT.

The paper evaluates the semantic stability of clinical LLMs to linguistic variations, finding that domain specialization does not guarantee consistent robustness improvements.

This study examines public perceptions of automated decision-making in healthcare using data from an ongoing longitudinal survey panel. Factors influencing perceived helpfulness, riskiness, and fairne…

The paper introduces SafeMed-R1, a clinically audited LLM that significantly improves safety and ethical alignment for medical applications, matching or exceeding resident performance on safety-critic…

The paper proposes a novel data transformation framework that creates semantically rich, privacy-preserving numeric views of EHR data, enabling large-scale research while provably breaking patient lin…

This paper demonstrates that patient-facing RAG chatbots frequently expose sensitive system configurations, knowledge base details, and conversation history through client-server communication, posing…

The paper introduces HERALD, a token-level cryptographic redaction framework that encrypts only sensitive tokens in clinical text, enabling privacy-preserving LLM deployment without significant loss o…

The paper proposes a novel cross-layer authentication framework for healthcare information exchange that combines initial PKI-based verification with continuous, lightweight physical layer feature ext…