JMIR AI

Shared decision-making is central to patient-centered care but is often hampered by AI systems that focus on technical transparency rather than delivering context-rich, clinically meaningful reasoning. Although XAI methods elucidate how decisions are made, they fall short in addressing the “why” that supports effective patient–clinician dialogue. To bridge this gap, we introduce AI-SDM, a conceptual framework designed to integrate AI-based reasoning into Shared decision-making to enhance care quality while preserving patient autonomy. AI-SDM is a structured, multi-model framework that synthesizes predictive modelling, evidence-based recommendations, and generative AI techniques to produce adaptive, context-sensitive explanations. The framework distinguishes conventional AI explainability from AI reasoning—prioritizing the generation of tailored, narrative justifications that inform shared decisions. A hypothetical clinical scenario in stroke management is used to illustrate how AI-SDM facilitates an iterative, triadic deliberation process between healthcare providers, patients, and AI outputs. This integration is intended to transform raw algorithmic data into actionable insights that directly support the decision-making process without supplanting human judgment.

Overcrowded emergency rooms might degrade the quality-of-care service and overload the clinic staff. Assessing unscheduled return visits (URVs) to the emergency department (ED) is a quality assurance procedure to identify ED discharged patients with a high likelihood of bounce-back, to ensure patient safety, and ultimately to reduce medical costs by decreasing the frequency of URVs. The field of machine learning (ML) has evolved considerably in the past decades and many ML applications have been deployed in various contexts.

Ultrasound examinations, while valuable, are time-consuming and often limited in availability. Consequently, many hospitals implement reservation systems; however, these systems typically lack prioritization for examination purposes. Hence, our hospital uses a waitlist system that prioritizes examination requests based on their clinical value when slots become available due to cancelations. This system, however, requires a manual review of examination purposes, which are recorded in free-form text. We hypothesized that AI language models could preliminarily estimate the priority of requests prior to manual reviews.

People Who Use Drugs (PWUD) are at heightened risk for severe injection-related infections. Current research relies on billing codes to identify PWUD—methodology that has suboptimal accuracy and potentially underestimates the economic, racial and ethnic diversity of hospitalized PWUD.

The digital transformation of health care has introduced both opportunities and challenges, particularly in managing and analyzing the vast amounts of unstructured medical data generated daily. There is a need to explore the feasibility of generative solutions in extracting data from medical reports, categorized by specific criteria.

MRI reports are challenging for patients to interpret and may subject patients to unnecessary anxiety. The advent of advanced artificial intelligence (AI) large language models (LLMs), such as GPT-4o, hold promise in translating complex medical information into layman terms.

The mechanisms underlying the mutual relationships between chronic physical illnesses and mental health disorders, which potentially explain their association, remain unclear. Furthermore, how patterns of this comorbidity evolve over time are under-investigated significantly.

Artificial intelligence (AI) is transforming medical imaging, with large language models such as ChatGPT-4 emerging as potential tools for automated image interpretation. While AI-driven radiomics has shown promise in diagnostic imaging, the efficacy of ChatGPT-4 in liver ultrasound analysis remains largely unexamined.

Generative AI (gAI), such as DALL-E 2, are promising tools that can generate novel images or artwork based on text input. However, caution is warranted as these tools generate information based on historical data and are thus at risk of propagating past learned inequities. Women in medicine have routinely been under-represented in academic and clinical medicine and the stereotype of a male physician persists.

There is considerable need to improve and increase the detection and measurement of depression. The use of speech as a digital biomarker of depression represents a considerable opportunity for transforming and accelerating depression identification and treatment; however, research to date has primarily consisted of small-sample feasibility or pilot studies incorporating highly controlled applications and settings. There has been limited examination of the technology in real-world use contexts.

The recent availability of law enforcement and coroner/medical examiner reports for nearly every violent death in the US expands the potential for natural language processing (NLP) research into violence.

Pharmacoepidemiologic studies, which promote rational drug use and improve health outcomes, often require Anatomical Therapeutic Chemical Classification System (ATC) drug classification within real-world data (RWD) sources. Existing classification tools are expensive, brittle, or have restrictive terms of service, and lack context that may inform classification itself.