Background: Medical coding structures healthcare data for research, quality monitoring, and policy. This study assesses the potential of large language models (LLMs) to assign ICPC-2 codes using the output of a domain-specific search engine. Methods: A dataset of 437 Brazilian Portuguese clinical expressions, each annotated with ICPC-2 codes, was used. A semantic search engine (OpenAI's text-embedding-3-large) retrieved candidates from 73,563 labeled concepts. Thirty-three LLMs were prompted with each query and retrieved results to select the best-matching ICPC-2 code. Performance was evaluated using F1-score, along with token usage, cost, response time, and format adherence. Results: Twenty-eight models achieved F1-score > 0.8; ten exceeded 0.85. Top performers included gpt-4.5-preview, o3, and gemini-2.5-pro. Retriever optimization can improve performance by up to 4 points. Most models returned valid codes in the expected format, with reduced hallucinations. Smaller models (<3B) struggled with formatting and input length. Conclusions: LLMs show strong potential for automating ICPC-2 coding, even without fine-tuning. This work offers a benchmark and highlights challenges, but findings are limited by dataset scope and setup. Broader, multilingual, end-to-end evaluations are needed for clinical validation.

Background: Large language models (LLMs) have demonstrated substantial potential to support clinical practice. Other than Chat GPT4 and its predecessors, few LLMs, especially those of the leading and more powerful reasoning model class, have been subjected to medical specialty examination questions, including in the domain of primary care. This paper aimed to test the capabilities of leading LLMs as of May 2025 (o3, Claude Opus 4, Grok3, and Gemini 2.5 Pro) in primary care education, specifically in answering Member of the Royal College of General Practitioners (MRCGP) style examination questions. Methods: o3, Claude Opus 4, Grok3, and Gemini 2.5 Pro were tasked to answer 100 randomly chosen multiple choice questions from the Royal College of General Practitioners GP SelfTest on 25 May 2025. Questions included textual information, laboratory results, and clinical images. Each model was prompted to answer as a GP in the UK and was provided with full question information. Each question was attempted once by each model. Responses were scored against correct answers provided by GP SelfTest. Results: The total score of o3, Claude Opus 4, Grok3, and Gemini 2.5 Pro was 99.0%, 95.0%, 95.0%, and 95.0%, respectively. The average peer score for the same questions was 73.0%. Discussion: All models performed remarkably well, and all substantially exceeded the average performance of GPs and GP registrars who had answered the same questions. o3 demonstrated the best performance, while the performances of the other leading models were comparable with each other and were not substantially lower than that of o3. These findings strengthen the case for LLMs, particularly reasoning models, to support the delivery of primary care, especially those that have been specifically trained on primary care clinical data.

Background: Hip replacement procedures improve patient lives by relieving pain and restoring mobility. Predicting hip replacement in advance could reduce pain by enabling timely interventions, prioritising individuals for surgery or rehabilitation, and utilising physiotherapy to potentially delay the need for joint replacement. This study predicts hip replacement a year in advance to enhance quality of life and health service efficiency. Methods: Adapting previous work using Temporal Graph Convolutional Neural Network (TG-CNN) models, we construct temporal graphs from primary care medical event codes, sourced from ResearchOne EHRs of 40-75-year-old patients, to predict hip replacement risk. We match hip replacement cases to controls by age, sex, and Index of Multiple Deprivation. The model, trained on 9,187 cases and 9,187 controls, predicts hip replacement one year in advance. We validate the model on two unseen datasets, recalibrating for class imbalance. Additionally, we conduct an ablation study and compare against four baseline models. Results: Our best model predicts hip replacement risk one year in advance with an AUROC of 0.724 (95% CI: 0.715-0.733) and an AUPRC of 0.185 (95% CI: 0.160-0.209), achieving a calibration slope of 1.107 (95% CI: 1.074-1.139) after recalibration. Conclusions: The TG-CNN model effectively predicts hip replacement risk by identifying patterns in patient trajectories, potentially improving understanding and management of hip-related conditions.

Digital technologies and artificial intelligence (AI) are advancing solutions in healthcare, becoming more prevalent in various sectors of the NHS. FREMONT, CA: Artificial intelligence (AI) and digital technology are improving healthcare solutions and spreading throughout the NHS. Artificial intelligence has assisted healthcare workers in shortening wait times, speeding up procedures, and enhancing early diagnosis. Over the past few years, secondary care has embraced AI for diagnoses, evaluating x-rays, and anticipating bed management. With over 337 million visits across England in 2021 alone, primary care is experiencing startlingly high demand.

TytoCare is a telehealth company using AI to transform primary care by putting health in the hands of consumers. TytoCare seamlessly connects people to clinicians to provide the best virtual home examination and diagnosis solutions. Its solutions are designed to enable a comprehensive medical exam from any location and include a hand-held, all-in-one tool for examining the heart, lungs, skin, ears, throat, abdomen, and body temperature; a complete telehealth platform for sharing exam data, conducting live video exams, and scheduling visits; a cloud-based data repository with analytics; and built-in guidance technology and machine learning algorithms to ensure accuracy and ease of use for patients and insights for healthcare providers. TytoCareS intends to reach underserved populations around the globe that lack access to basic medical care, and even in communities with minimal healthcare infrastructure. TytoCare's solution democratizes access to high-quality healthcare, regardless of geography, with a handheld examination kit that enables users to perform comprehensive remote physical exams of the heart, skin, ears, throat, abdomen, and lungs, and measure heart rate and body temperature, which are key for treating many acute and chronic conditions.

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To create a more efficient and effective healthcare system, three technical challenges immediately present themselves: healthcare access, healthcare equity, and healthcare efficiency. An autonomous mobile clinic solves the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an AI doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the autonomous mobile clinic network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system.

Founded in 2020, WellAI, an AI health-tech company, is the developer of scientifically and technologically advanced medical applications. WellAI's engineers, fresh off the development of a COVID-19 research tool (presented at the IFCC annual conference) leveraged their expertise into developing an advanced clinical diagnostic tool (triage solution) for physicians, caregivers, and employees/individuals. The company is the developer of the Digital Health Triage Assistant, WellAI for Medical Providers, and the Adaptive AI Diagnostic Engine. It also provides custom solutions. The AI Diagnostic Engine has uniquely assimilated 30 million medical studies and has the ability to diagnose, with 83% average accuracy, more than 500 health conditions including pediatric specific conditions using simple spoken language in less than 1 minute.

In health care, the days of business as usual are over. Around the world, every health care system is struggling with rising costs and uneven quality despite the hard work of well-intentioned, well-trained clinicians. Health care leaders and policy makers have tried countless incremental fixes--attacking fraud, reducing errors, enforcing practice guidelines, making patients better "consumers," implementing electronic medical records--but none have had much impact. At its core is maximizing value for patients: that is, achieving the best outcomes at the lowest cost. We must move away from a supply-driven health care system organized around what physicians do and toward a patient-centered system organized around what patients need. We must shift the focus from the volume and profitability of services provided--physician visits, hospitalizations, procedures, and tests--to the patient outcomes achieved. And we must replace today's fragmented system, in which every local provider offers a full range of services, with a system in which services for particular medical conditions are concentrated in health-delivery organizations and in the right locations to deliver high-value care. Making this transformation is not a single step but an overarching strategy. We call it the "value agenda." It will require restructuring how health care delivery is organized, measured, and reimbursed. In 2006, Michael Porter and Elizabeth Teisberg introduced the value agenda in their book Redefining Health Care. Since then, through our research and the work of thousands of health care leaders and academic researchers around the world, the tools to implement the agenda have been developed, and their deployment by providers and other organizations is rapidly spreading. The transformation to value-based health care is well under way. Some organizations are still at the stage of pilots and initiatives in individual practice areas. Other organizations, such as the Cleveland Clinic and Germany's Schön Klinik, have undertaken large-scale changes involving multiple components of the value agenda. The result has been striking improvements in outcomes and efficiency, and growth in market share. There is no longer any doubt about how to increase the value of care. The question is, which organizations will lead the way and how quickly can others follow? The challenge of becoming a value-based organization should not be underestimated, given the entrenched interests and practices of many decades. This transformation must come from within.

Dr. Steven Waldren, vice president and chief informatics officer at the American Academy of Family Physicians, right, and Dr. Kamel Sadek, director of informatics at Village Medical, speak at the HIMSS22 conference in Orlando. ORLANDO, Fla. – Even before COVID-19 made the business of healthcare a nightmare for countless physicians and clinicians, burnout was a prevalent issue. And even the slow, still-ongoing emergence into normalcy hasn't been enough to ease this trend: Clerical burdens, including clinical documentation, are a major contributor. But for primary care physicians in particular, a new class of technology, including AI-powered digital assistants, is improving their capacity and capability, while reducing their administrative and cognitive burden. Dr. Steven Waldren, vice president and chief informatics officer at the American Academy of Family Physicians, cited data showing that the average patient visit to a PCP takes about 18 minutes, and of that time, 27% is dedicated to face-to-face time with a patient.

Artificial intelligence can alleviate administrative burdens, improve diagnostic accuracy, identify patients most at risk for certain diseases and reduce unnecessary procedures, according to a recent paper. Yet, "most primary care providers do not know what it is, how it will impact them and their patients and what its key limitations and ethical pitfalls are," Steven Lin, MD, the author of the paper and family medicine service chief and head of technology innovation in the division of primary care and population health at Stanford Medicine, wrote in the Journal of the American Board of Family Medicine. He added that primary care is the ideal medical specialty to take charge in what he called the "health care artificial intelligence (AI) revolution." Lin shared more details on this emerging technology and how primary care can maximize its potential in an interview with Healio. Healio: Why should primary care lead the "health care AI revolution"?