I ntelligent drug recommendation based on Electronic Health Records (EHRs) is critical for improving the quality and efficiency of clinical decision - making . By leveraging large - scale patient data, drug recommendation systems can assist physicians in selecting the most appropriate medications according to a patient's medical history, diagnoses, laboratory results, and comorbidities. Recent advances in large language models (LLMs) have shown remarkable capabilities in complex reasoning and medical text understanding, making them promising tools for drug recommendation tasks. However, the application of LLMs for Chinese clinical medication recommendation remains l argely unexplored. In this work, we conduct a systematic investigation of LLM - based methodologies for Chinese discharge medication recommendation . W e evaluate several representative LLM families (GLM, Llama, Qwen) under a unified methodological framework including zero - shot prompting, in - context learning, chain - of - thought prompting, and supervised fine - tuning using LoRA. W e analyze model behavior acro ss reasoning styles, error patterns, domain adaptation mechanisms, and robustness . Experimental results show that while supervised fine - tuning improves model performance, there remains substantial room for improvement, with the best model achieving the F1 score of 0.5648 and Jaccard score of 0.4477 . Our findings highlight both the potential and limitations of LLMs for Chinese drug recommendation.

The application of AI in psychiatric diagnosis faces significant challenges, including the subjective nature of mental health assessments, symptom overlap across disorders, and privacy constraints limiting data availability. To address these issues, we present MoodAngels, the first specialized multi-agent framework for mood disorder diagnosis. Our approach combines granular-scale analysis of clinical assessments with a structured verification process, enabling more accurate interpretation of complex psychiatric data. Complementing this framework, we introduce MoodSyn, an open-source dataset of 1,173 synthetic psychiatric cases that preserves clinical validity while ensuring patient privacy. Experimental results demonstrate that MoodAngels outperforms conventional methods, with our baseline agent achieving 12.3% higher accuracy than GPT-4o on real-world cases, and our full multi-agent system delivering further improvements. Evaluation in the MoodSyn dataset demonstrates exceptional fidelity, accurately reproducing both the core statistical patterns and complex relationships present in the original data while maintaining strong utility for machine learning applications. Together, these contributions provide both an advanced diagnostic tool and a critical research resource for computational psychiatry, bridging important gaps in AI-assisted mental health assessment.

Artificial intelligence can predict people's health problems over a decade into the future, say scientists. The technology has learned to spot patterns in people's medical records to calculate their risk of more than 1,000 diseases. The researchers say it is like a weather forecast that anticipates a 70% chance of rain - but for human health. Their vision is to use the AI model to spot high-risk patients to prevent disease and to help hospitals understand demand in their area, years ahead of time. The model - called Delphi-2M - uses similar technology to well-known AI chatbots like ChatGPT.

There was a time, just a couple of decades ago, when researchers in psychology and health always had to engage with people face-to-face or using the telephone. The worst case scenario was sending questionnaire packs out to postal addresses and waiting for handwritten replies. So we either literally met our participants, or we had multiple corroborating points of evidence that indicated we were dealing with a real person who was, therefore, likely to be telling us the truth about themselves. Since then, technology has done what it always does – creating opportunities for us to cut costs, save time and access wider pools of participants on the internet. But what most people have failed to fully realise is that internet research has brought along risks of data corruption or impersonation which could be deliberately aiming to put research projects in jeopardy.

The Trump administration is pushing an initiative for millions of Americans to upload personal health data and medical records on new apps and systems run by private tech companies, promising easier to access health records and wellness monitoring. Donald Trump is expected to deliver remarks on the initiative on Wednesday afternoon in the East Room. The event is expected to involve leaders from more than 60 companies, including major tech companies such as Google and Amazon, as well as prominent hospital systems like the Cleveland clinic. The new system will focus on diabetes and weight management, conversational artificial intelligence that helps patients, and digital tools such as QR codes and apps that register patients for check-ins or track medications. The initiative, spearheaded by an administration that has already freely shared highly personal data about Americans in ways that have tested legal bounds, could put patients' desires for more convenience at their doctor's office on a collision course with their expectations that their medical information be kept private.

Health and Human Services Secretary Robert F. Kennedy Jr. shares his journey to his official position and where his passion for health comes from on'My View with Lara Trump.' The White House revealed new details Wednesday regarding the Trump administration's efforts to advance healthcare technology and partnerships with private-sector technology companies. The "Make Health Tech Great Again" event was expected to provide more details on how the administration is advancing a "next-generation digital health ecosystem," after securing partnerships with companies including Amazon, Anthropic, Apple, Google, and OpenAI to better share information between patient and providers within Medicare and Medicaid services. U.S. Health and Human Services Secretary Robert F. Kennedy Jr., announced that the HHS will ban illegal immigrants from accessing taxpayer-funded programs. "For decades, bureaucrats and entrenched interests buried health data and blocked patients from taking control of their health," Department of Health and Human Services Secretary Robert F. Kennedy, Jr. said in a statement Wednesday ahead of the event.

Introduction: Electronic health records (EHR) are a critical medium through which patient stigmatization is perpetuated among healthcare teams. Methods: We identified linguistic features of doubt markers and stigmatizing labels in MIMIC-III EHR via expanded lexicon matching and supervised learning classifiers. Predictors of rates of linguistic features were assessed using Poisson regression models. Results: We found higher rates of stigmatizing labels per chart among patients who were Black or African American (RR: 1.16), patients with Medicare/Medicaid or government-run insurance (RR: 2.46), self-pay (RR: 2.12), and patients with a variety of stigmatizing disease and mental health conditions. Patterns among doubt markers were similar, though male patients had higher rates of doubt markers (RR: 1.25). We found increased stigmatizing labels used by nurses (RR: 1.40), and social workers (RR: 2.25), with similar patterns of doubt markers. Discussion: Stigmatizing language occurred at higher rates among historically stigmatized patients, perpetuated by multiple provider types.

Medical consultation dialogues contain critical clinical information, yet their unstructured nature hinders effective utilization in diagnosis and treatment. Traditional methods, relying on rule-based or shallow machine learning techniques, struggle to capture deep and implicit semantics. Recently, large pre-trained language models and Low-Rank Adaptation (LoRA), a lightweight fine-tuning method, have shown promise for structured information extraction. We propose EMRModel, a novel approach that integrates LoRA-based fine-tuning with code-style prompt design, aiming to efficiently convert medical consultation dialogues into structured electronic medical records (EMRs). Additionally, we construct a high-quality, realistically grounded dataset of medical consultation dialogues with detailed annotations. Furthermore, we introduce a fine-grained evaluation benchmark for medical consultation information extraction and provide a systematic evaluation methodology, advancing the optimization of medical natural language processing (NLP) models. Experimental results show EMRModel achieves an F1 score of 88.1%, improving by49.5% over standard pre-trained models. Compared to traditional LoRA fine-tuning methods, our model shows superior performance, highlighting its effectiveness in structured medical record extraction tasks.

With just one click, the AI technology begins transcribing the doctor's conversation with a patient. DENVER – Artificial intelligence is quietly transforming how doctors interact with patients -- and it might already be in use during your next visit to the doctor's office. Thousands of physicians across the country are using a form of AI called ambient listening, surveys show. This technology listens to conversations between doctors and patients, creates real-time transcriptions, and then compiles detailed clinical notes -- all without disrupting the flow of the appointment. Dr. Daniel Kortsch, associate chief of artificial intelligence and digital health at Denver Health, said that ambient listening technology has made a big difference since his practice began using it in fall 2024.

General practitioners (GPs) serve as the cornerstone of primary healthcare systems by providing continuous and comprehensive medical services. However, due to community-oriented nature of their practice, uneven training and resource gaps, the clinical proficiency among GPs can vary significantly across regions and healthcare settings. Currently, Large Language Models (LLMs) have demonstrated great potential in clinical and medical applications, making them a promising tool for supporting general practice. However, most existing benchmarks and evaluation frameworks focus on exam-style assessments-typically multiple-choice question-lack comprehensive assessment sets that accurately mirror the real-world scenarios encountered by GPs. To evaluate how effectively LLMs can make decisions in the daily work of GPs, we designed GPBench, which consists of both test questions from clinical practice and a novel evaluation framework. The test set includes multiple-choice questions that assess fundamental knowledge of general practice, as well as realistic, scenario-based problems. All questions are meticulously annotated by experts, incorporating rich fine-grained information related to clinical management. The proposed LLM evaluation framework is based on the competency model for general practice, providing a comprehensive methodology for assessing LLM performance in real-world settings. As the first large-model evaluation set targeting GP decision-making scenarios, GPBench allows us to evaluate current mainstream LLMs. Expert assessment and evaluation reveal that in areas such as disease staging, complication recognition, treatment detail, and medication usage, these models exhibit at least ten major shortcomings. Overall, existing LLMs are not yet suitable for independent use in real-world GP working scenarios without human oversight.