Journal of Medical Internet Research

Intravenous fluid therapy is a cornerstone of optimizing body hydration, circulation, and perfusion, yet its rational implementation is limited by inconsistent clinical terminology. As fluid therapy moves toward AI-empowered clinical decision support systems (AI-CDSS), there is a critical need for high-quality, verified training data. The reliability of these systems is currently threatened by misleading data resulting from ambiguous definitions and the interchangeable use of nonsynonymous terms, such as “dehydration” and “hypovolemia.” This multidisciplinary panel-of-experts opinion article intends to start a discussion by identifying the key drawbacks in current terminology and proposes solutions, including a shift toward adequacy-based phenotypes and categorizing blood and water volumes as “insufficient,” “optimal,” or “excessive” rather than relying on context-sensitive quantitative measures such as “low,” “normal,” or “high.” Illustrative scenarios are used to show how inconsistent terminology and static quantitative labels could mislead an AI-CDSS. The proposed phenotype matrix is designed to provide a structured, systematic description of fluid requirements for specific clinical objectives that can be integrated into clinical algorithms and machine learning databases. An example of a fluid requirement assessment algorithm is used for exploring its applicability in AI training. The proposed terminology and phenotype matrix are provisional: they represent the opinion of a multidisciplinary expert panel and have not undergone formal consensus, clinical validation, or testing within an AI system. Broad consensus-building initiatives will follow.

Clinical large language model agents depend on both model capability and the surrounding system that selects information, constrains actions, and verifies execution. In this Viewpoint, we argue that this harness should be an explicit unit of clinical agent design, evaluation, and reporting. Our aim is to help clinical informatics and development teams specify this layer and to help evaluators and institutional governance teams assess its contributions and risks. Selected benchmarks show substantial performance differences across harness configurations, but architecture comparisons, bundled improvements, and within-model component comparisons support different levels of causal attribution. We propose a revisable 3-tier framework: tier 1 specifies general orchestration functions, including prompts, tools, output schemas, control flow, memory, and generic verification; tier 2 specifies their clinical adaptations for knowledge integration, differential reasoning, missingness and temporality, safety verification, and workflow alignment; and tier 3 specifies a failure-driven evaluation cycle applied to both. Our contribution is a framework that makes these connections explicit. We provide classification rules, an illustrative clinical example, a proposed evaluation protocol, and a minimum reporting checklist. Observable execution traces are distinguished from private model reasoning, with provenance and privacy safeguards supporting retrospective reconstruction. Evaluation should separate task success, clinical correctness, process safety, and operational reliability. Lightweight fine-tuning can complement harness design; their interaction requires controlled study. The framework is a proposal for empirical testing, not a validated taxonomy or evidence of deployment safety.

AI-enabled digital medicine platforms in Europe are developed within overlapping rules for personal data, medical devices, AI, clinical research, health data access, and health technology assessment. This viewpoint represents the authors’ experience and interpretation from the preimplementation phase of INTERCEPT, an Innovative Health Initiative consortium that plans to validate biomarkers and develop a blood-based risk score for early Crohn disease interception. It does not report original empirical research, a systematic regulatory review, a deployed platform, or a validated AI model. Our aim is to explain how compliance-by-design can be translated into an auditable governance architecture and to identify what still requires scientific evaluation. We advance 3 messages. First, governance must begin with intended purpose and boundary mapping: a digital platform may contain research, operational, and medical device software functions, and conformity of one module cannot be inferred for the platform as a whole. Second, overlapping obligations can be operationalized through 5 linked controls: modular responsibility and change control, federated data governance, traceable AI life cycle management, consent and participant rights orchestration, and a common cross-border governance core with local adaptations. Third, compliance is not a surrogate for model validity, clinical benefit, trust, scalability, or market access. Before clinical deployment, planned AI components require prespecified labels and cohorts, leakage prevention, missing data and imbalance strategies, calibration, subgroup assessment, external validation, human factors evaluation, and postdeployment algorithmovigilance. A hypothetical biomarker risk score update illustrates how the 5 controls interact. INTERCEPT is informative because it combines cross-border biomarker research, planned predictive analytics, and heterogeneous institutional settings while key design decisions remain modifiable. Its contribution is nevertheless conceptual: the proposed framework is a testable governance hypothesis, not evidence of implementation success. Prospective studies should compare it with alternative governance models and assess safety, workload, participant experience, evidence generation, and regulatory performance.

The proliferation of electronic clinical information resources has transformed how evidence is accessed during primary care consultations, particularly in community settings where clinicians must rapidly access reliable information at the point of care (POC). For general practice trainees, who are still developing clinical expertise and information-seeking skills, how these digital resources are used during real-time decision-making remains poorly understood.

In response to a recently published Delphi study on AI in spiritual care, this commentary focuses on ways to perceive the relationship between AI and human beings in the context of spiritual care. We explore this relationship by framing it within the philosophy of Martin Buber. Because AI has no lived experience or existential crises of its own, we question whether a truly fulfilling encounter between a human being and an AI model is possible when the relationship is inherently asymmetrical on an existential level.

Machine learning models are increasingly used for glycemic event prediction and continuous glucose forecasting, but targets, populations, horizons, validation, and uncertainty reporting remain heterogeneous.

Accurate interpretation of pancreatic cancer status and resectability-related information, together with effective perioperative communication, remains a major challenge in pancreatic cancer care. In routine practice, unstructured computed tomography (CT) reports and complex clinical information may limit the accessibility and consistency of clinical information delivery and increase the communication burden on clinicians. Large language models (LLMs) offer new opportunities for clinical information support; however, existing applications are often task-specific and rarely combine CT report interpretation with perioperative consultation within a unified patient-oriented framework.

Patient education materials (PEMs) are essential for improving health literacy and clinical outcomes. Although large language models (LLMs) hold promise for optimizing content, their performance in generating new PEMs remains unclear.

Effective communication between clinicians and patients plays an important role in the successful delivery of health care. In geriatric medicine, where multimorbidity, polypharmacy, and functional limitations often make treatment plans complex, AI simplifications of clinical reports into patient-friendly language offer a promising way to improve communication.

The rapid advancement of generative AI (GenAI) holds transformative potential for clinical nursing practice, yet its integration also brings significant risks and uncertainties. Despite growing discourse on AI in health care, frontline nurses’ firsthand experiences, perceptions, and practical needs regarding GenAI remain largely under-synthesized in the existing literature.

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.