Interpersonal and professional qualities of physicians profoundly shape patient trust, communication, adherence, and health outcomes [1, 2]. Understanding these qualities from the patient's perspective is essential to advancing patient-centered care, yet current measurement tools--such as standardized surveys or aggregate star ratings--capture only a narrow view of the physician-patient relationship. In parallel, millions of online physician reviews now provide an abundant, patient-generated record of real-world experiences, offering an unprecedented opportunity to examine how physicians are perceived in everyday practice [3, 4, 5, 6]. Extracting clinically meaningful information from such narrative data remains challenging. Prior studies have typically relied on sentiment analysis or topic modeling, approaches that overlook the multidimensional nature of patient perceptions. Well-established frameworks from psychology, such as the Big Five personality traits [7], offer interpretable constructs for describing interpersonal style, but have rarely been operationalized at scale in healthcare settings [8]. Similarly, healthcare-specific qualities--communication effectiveness, perceived competence, attentiveness to outcomes, and trustworthiness--are widely recognized as central to care quality but are difficult to measure systematically. Manual coding of these traits is costly, inconsistent, and infeasible for national datasets. Recent advances in large language models (LLMs) enable a new approach [9].

Funding The study was supported by the Start - up Fund for RAPs under the Strategic Hiring Scheme (P0048623) from HKSAR, the Global STEM Professorship Scheme (P0046113) and Henry G. Leong Endowed Professorship in Elderly Vision Health. 2 Abstract Large language models (LLMs) show promise for tailored healthcare communication but face challenges in interpretability and multi - task integration particularly for domain - specific needs like myopia, a nd their real - world effectiveness as patient education tools has yet to be demonstrated . Here, we introduce ChatMyopia, an LLM - based AI agent designed to address text and image - based inquiries related to myopia. To achieve this, ChatMyopia integrates an image classification tool and a retrieval - augmented knowledge base built from literature, expert consensus, and clinical guidelines. M yopic maculopathy grading task, single question examination and human evaluations validated its ability to deliver personalized, accurate, and safe responses to myopia - related inquirie s with high scalability and interpretability . In a randomized controlled trial (n=70, NCT06607822), ChatMyopia significantly improved patient satisfaction compared to traditional leaflets, enhancing patient education in accuracy, empathy, disease awareness, and patient - eye care practitioner communication. These findings highlight ChatMyopia ' s potential as a valuable supplement to enhance patient education and improve satisfaction with medical services in primary eye care settings . Keywords: Large language model, Medical a gent, Myopia, Patient education, Randomized controlled trial. Introduction For patients, a lack of basic understanding of their condition before initial consultations can hinder communication, as clinicians may spend time explaining fundamental concepts instead of critical issues, resulting in poor decisions and noncompliance [1, 2] . Therefore, patients require professional information and support to enhance their healthcare experiences.

Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey methods often fall short due to limited scope, time, and spatial coverage. Crowdsourcing through online reviews or social media offers a valuable approach to gaining such insights. With recent advancements in large language models (LLMs), extracting nuanced perceptions from reviews has become feasible. This study collects Google Maps reviews across the DMV and Florida areas and conducts prompt engineering with the GPT model to analyze the aspect-based sentiment of urgent care. We first analyze the geospatial patterns of various aspects, including interpersonal factors, operational efficiency, technical quality, finances, and facilities. Next, we determine Census Block Group(CBG)-level characteristics underpinning differences in public perception, including population density, median income, GINI Index, rent-to-income ratio, household below poverty rate, no insurance rate, and unemployment rate. Our results show that interpersonal factors and operational efficiency emerge as the strongest determinants of patient satisfaction in urgent care, while technical quality, finances, and facilities show no significant independent effects when adjusted for in multivariate models. Among socioeconomic and demographic factors, only population density demonstrates a significant but modest association with patient ratings, while the remaining factors exhibit no significant correlations. Overall, this study highlights the potential of crowdsourcing to uncover the key factors that matter to residents and provide valuable insights for stakeholders to improve public satisfaction with urgent care.

Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public databases. Additionally, this module features capabilities for processing long context. The EICL module aids in generating sentences with high attribute relevance by memorizing semantic and attribute information from unlabelled corpora and applying controlled retrieval for the required information. Furthermore, a dataset comprising 803,564 consultation records was compiled in China, significantly enhancing the model's capability for complex dialogues and proactive inquiry initiation. Comprehensive experiments demonstrate the proposed method's effectiveness in extending the context window length of existing LLMs. The experimental outcomes and extensive data validate the framework's superiority over five baseline models in terms of BLEU and ROUGE performance metrics, with substantial leads in certain capabilities. Notably, ablation studies confirm the significance of the TEIR and EICL components. In addition, our new framework has the potential to significantly improve patient satisfaction in real clinical consulting situations.

The healthcare industry and national healthcare systems across the world have experienced unprecedented pressure in recent years, especially during the Covid-19 pandemic. Many HealthTech start-ups started investing in healthcare technology in the early 2010s, but the crisis in 2020 inevitably accelerated digital transformation to allow medical professionals to keep seeing patients during the lockdowns. Recent research by Virgin Media Business, examining the use of information technology in healthcare in the UK, found that the benefits of digital transformation in operational areas are already showing. For example, AI is helping to streamline patient triage, enabling doctors to treat urgent cases more rapidly with lifesaving outcomes. But what will be the real impact of information technology in healthcare going forward?

Patient satisfaction is a top priority for many hospitals and healthcare organisations. With machine learning and (AI) patient data can become invaluable, providing insights into where improvement in the patient journey is needed. Machine learning systems provide an opportunity for hospitals to improve overall health outcomes, as patient satisfaction is strongly associated with greater compliance and increased treatment adherence, according to researchers. AI can also provide more personalised and convenient healthcare experiences. Chatbots used by healthcare organisations can also boost patient satisfaction.

The advent of Electronic Health Record systems and their accompanying documentation has created a deep fissure within the medical community. Epidemic-level numbers show that more and more physicians report feeling burnt out and depressed. The overall rate of work-life happiness reported by healthcare providers dropped below 50% thanks to the pandemic. Numbers released in Medscape's 2021 physician lifestyle report state that 43% of all physicians report feeling burnt out. Of those burnt-out physicians, 58% say they feel that way due to the long list of bureaucratic tasks like note taking and EHR documentation.

Italian medical implant manufacturer REJOINT is introducing mass customization and therapy personalization through a combination of Electron Beam Melting (EBM) and computerized analysis of intraoperative and post-operative data collection through IoT-connected sensorized wearables. The market for knee implants is now estimated at around five million implants per year worldwide. In advanced markets, already in 2011 the number of surgical procedures was 150 per 100,000 inhabitants, with peaks of 250 in some markets such as Austria and Switzerland. The strongest annual increase (7%) occurred in patients 64 years and under 1. The knee arthroplasty market until recently solely consisted of standard prosthetic systems, with a limited range of sizes available.

AI-powered EHR systems seamlessly integrate and offer solutions with a variety of functionalities. Machine learning and Natural Language Processing (NLP) can help in recording the medical experiences of the patients, organizing the large EHR data banks for finding important documents, gauging patient satisfaction, etc. The machine learning models merged with NLP can help healthcare providers in transcribing the speech from the voice recognition system into text. The algorithms can be trained well on large volumes of patient data on patient's treatment, equipment used for treatment, respective doctor, etc and carefully segmented based upon the individual patient, illness, treatment for illness, etc. This will enhance the document and information search from the large databases.

Artificial Intelligence (AI) tools are set to change the way medicine is practiced. In his book Deep Medicine, Eric Topol argues that AI can change medicine for the better, if implemented in a way that focuses on improving the doctor-patient connection. Here we look at 7 ways in which AI can improve the patient experience. A number of algorithms show promise when it comes to medical diagnoses.1 Generally, these are Machine Learning (ML) algorithms, such as neural networks or clustering algorithms, where a computer is trained on a very large data set.