Journal of Medical Internet Research

The American Society of Anesthesiologists Physical Status (ASA-PS) classification system fundamentally shapes perioperative care delivery but suffers from poor interrater reliability (0.4‐0.6). Machine learning (ML) models and large language models (LLMs) offer complementary remedies: ML processes structured data consistently but cannot interpret the nuance of clinical narratives, while LLMs parse narrative text and generate explanations but adhere inconsistently to diagnostic criteria.

Mobility recovery after critical illness is a complex and multidimensional process. Digital health technologies have emerged as approaches for delivering rehabilitation, monitoring recovery, and facilitating self-management among intensive care unit (ICU) survivors. However, the extent to which current digital health approaches address the multidimensional nature of mobility recovery remains unclear.

Women with polycystic ovary syndrome (PCOS) who achieve clinical pregnancy following fresh in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) carry an elevated miscarriage risk. However, individualized risk stratification after clinical pregnancy confirmation but before outcome determination remains challenging in clinical practice.

Advances in the diagnosis and treatment of childhood cancer have substantially improved survival, resulting in a growing population of survivors of childhood cancer who may experience long-term treatment-related sequelae. Cognitive late effects are an important long-term consequence of childhood cancer and can affect cognitive functioning, psychosocial well-being, and quality of life. Digital health interventions offer accessible and flexible approaches to managing cognitive difficulties among children with cancer. However, the existing evidence has not yet been comprehensively synthesized.

Frontline health workers in low- and middle-income countries (LMICs) often face barriers that compromise care quality, including limited training, high patient loads, inadequate access to updated clinical guidance, and resource constraints. Working in underserved settings further exacerbates challenges to providing accurate, complete, and empathetic responses. Addressing these gaps requires innovative tools to support FLWs in real time.

Clinical communication skills are a core yet complex competency in medical education and practice. However, early-stage medical students often struggle to apply communication frameworks in practice due to limited opportunities for structured simulation training. Generative AI (GenAI)–supported training has the potential to offer a scalable and innovative approach for clinical communication skills training.

Large language models (LLMs) show great potential for widespread application in the field of nursing; however, their limitations include hallucinations, insufficient domain knowledge, and delays in updating evidence. Retrieval-augmented generation (RAG), by combining external knowledge retrieval with text generation, holds promise for optimizing model outputs. However, current evidence on the use of RAG in nursing remains fragmented and has not been systematically synthesized.

Online peer-support communities have become an increasingly important source of mental health information and support, particularly for individuals experiencing depression and anxiety. Platforms such as Reddit provide anonymous, accessible spaces where users can seek advice, share experiences, and receive community feedback. While these communities may help reduce barriers associated with stigma and access to care, concerns remain regarding the reliability, safety, and clinical appropriateness of peer-generated advice. Despite their widespread use, little empirical evidence exists regarding the extent to which highly endorsed online advice reflects evidence-based clinical recommendations.

Postoperative mortality remains a key indicator of surgical safety and quality. Traditional risk models based on static preoperative or intraoperative variables often fail to capture dynamic physiological changes during surgery and generalize poorly across surgical types. To address these limitations, we developed and validated a deep learning framework capable of learning heterogeneous temporal patterns from intraoperative signals while maintaining interpretability and adaptability.

Pediatric care resources are limited and unevenly distributed in many health systems. Caregivers increasingly turn to large language models (LLMs) for children’s health information, but real-world evidence on LLM use and response quality in general pediatric care is limited.

Digital decision-support tools, such as the Stroke Triage App (STA), may enhance prehospital triage and clinical decision-making for suspected patients with stroke. However, implementation of decision-support tools and their impact on clinical practice remains insufficiently studied.

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) represent critical windows for early neurocognitive intervention, although access to timely, personalized rehabilitation remains limited. Digital health technologies offer opportunities to deliver scalable and individualized cognitive care; however, their successful implementation depends on an understanding of users’ needs, expectations, and barriers. Within a human-centered design (HCD) framework, actively involving end users during the earliest stages of development is essential to ensure that digital rehabilitation is acceptable, usable, and responsive to real-world rehabilitation needs.