Medicolegal

Dataset documentation for responsible AI: analysis of suitability and usage for health datasets.

Artificial Intelligence (AI) is rapidly transforming healthcare, but also raising concerns about algorithmic biases that mostly stem from the training data. It is widely supported that transparent dataset documentation is key to enabling responsible AI development. Several standardized dataset documentation approaches have been established, such as Datasheet, Dataset Nutrition Label, Accountabilit...

Automated phenotyping of ophthalmologic diseases from routine medical records using small language models and the human phenotype ontology (HPO).

Automated phenotyping in ophthalmology requires accurate standardization of clinical terms to facilitate interoperability and research. This study evaluates the suitability of the human phenotype ontology (HPO) for automated extraction of ophthalmic phenotypes from narrative documentation. We developed a locally operated AI pipeline combining text segmentation and negation detection based on a sma...

Nurses' experiences regarding role boundaries in collaborative AI-Assisted nursing: A meta-synthesis.

BACKGROUND: Artificial intelligence (AI) is rapidly integrating into the nursing field, evolving from simple assistive tools to complex applications t...

From decision support to clinical integration: A scoping review of artificial intelligence in prehospital airway management.

BACKGROUND: Airway management is a critical component of prehospital emergency care, where rapid decision-making and procedural accuracy are essential...

[Use of artificial intelligence for next-gen anamnesis and communication in orthopedics & trauma surgery : From chatbots to ambient intelligence].

BACKGROUND: Against the backdrop of increasing patient volumes, rising case complexity, and physicians' limited time, AI-driven systems for anamnesis,...

The detectability paradox: bilingual medical report generation with open-weight models and the limits of human oversight.

OBJECTIVES: The automation of medical report generation using large language models (LLMs) could significantly reduce physicians' documentation burden...

Artificial Intelligence for Regulatory Evidence: A Systematic Document Analysis of European Medicines Agency Regulatory Advice and Public Reports.

Artificial intelligence (AI) is receiving increasing attention across the entire lifecycle of medicines, from early development to postauthorization u...

Impact of a Prototype Combining Recommender Functionality With Structured Documentation on Operator Performance in Calls to Medical Communication Centers: Quasi-Experimental Feasibility Study.

BACKGROUND: Management of contacts to medical communication centers relies heavily on clinical judgment, contextual understanding, and communication s...

Clinical Context Variables Collectively Rival Model Choice in Embedding-Based Retrieval: Multi-Corpus Benchmark Study.

BACKGROUND: Retrieval-augmented generation (RAG) systems increasingly support clinical decision-making by grounding large language model outputs in ve...

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.

OBJECTIVES: To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage...

Ai-generated personalized informed consent for elective spinal surgery: a comparative study using retrieval-augmented generation.

BACKGROUND: Informed consent (IC) documents in spine surgery frequently lack procedure-specific risk data, quantitative complication rates, and discus...

Emerging Applications and Key Considerations on the Use of Artificial Intelligence in Forensic Medicine and Pathology.

Artificial intelligence (AI) has rapidly expanded across medicine, demonstrating value in image analysis, risk prediction, and data interpretation. In...

The Role of Digital Pathology in Optimizing the Quality and Timeliness of Intraoperative Consultation.

BackgroundIntraoperative consultation using frozen sections has been crucial for guiding surgical decisions, but has often been limited by the time an...

Challenges of AI-generated stigmatizing language regarding substance use disorders.

Stigmatizing language describing substance use behaviors in clinical documentation and in patient education materials can harm patients and their fami...

Building Toward a Future for Electronic Health Record Systems.

Health information technology tools, including electronic health records, are ubiquitous in healthcare across the United States. Despite the promise a...

Self-regulating the use of large language models in clinical practice: a risk-stratified approach.

The rapid integration of large language models (LLMs) into clinical practice offers promising benefits, including assistance with documentation, decis...

Machine learning and response surface methodology for optimization and prediction of tribological performance of PLA/rice husk biochar composites.

The present study investigates the tribological behaviour of polylactic acid (PLA) composites reinforced with rice husk biochar (RHBC) through an inte...

Artificial intelligence in general surgery: A national survey on knowledge, utilization, and future expectations.

Artificial intelligence is rapidly expanding across medical fields, yet its integration into surgical practice remains limited. Understanding surgeons...

The Application of Machine Learning in the Development of Co-Amorphous Dry Powder Inhalation.

Besides improving drug solubility and stability, co-amorphous systems (COAMS) have recently been reported to enhance the pulmonary delivery efficiency...

Optimisation Drift and Substitution Risk in Artificial Intelligence-Supported Personalised Mental Health Nursing: A Critical Synthesis on Therapeutic Presence and Care Biography.

INTRODUCTION: Artificial intelligence-supported personalisation is accelerating in mental health services, yet it can reclassify relationship, biograp...