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Response to “Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine”

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

This study explores the potential applications and associated safety concerns of large language models in healthcare. It particularly highlights the multifaceted applications of large language models (e.g., ChatGPT, MedLM) in healthcare, including data analysis, diagnostics, information retrieval, usage of medical devices, and assistance in tasks. Concurrently, it underscores the risks present alongside these applications, especially concerning data privacy. The study emphasizes the necessity of data privacy protection throughout the entire cycle. By reviewing policies from various countries, it outlines the critical role of refined policies and a clear governmental stance in advancing the application of large language models in healthcare and ensuring their safety.

This study aims to evaluate physicians' practices and perspectives regarding large language models (LLMs) in health care settings.A cross-sectional survey study was conducted between May and July 2024, comparing physician perspectives at two major academic medical centers (AMCs), one with institutional LLM access and one without. Participants included both clinical faculty and trainees recruited through departmental leadership and snowball sampling. Primary outcomes were current LLM use frequency, ranked importance of evaluation metrics, liability concerns, and preferred learning topics.Among 306 respondents (217 attending physicians [70.9%], 80 trainees [26.1%]), 197 (64.4%) reported using LLMs. The AMC with institutional LLM access reported significantly lower liability concerns (49.2 vs. 66.7% reporting high concern; 17.5 percentage points difference [95% CI, 6.8-28.2]; p = 0.0082). Accuracy was prioritized across all specialties (median rank 1.0 [interquartile range; IQR, 1.0-2.0]). Of the respondents, 287 physicians (94%) requested additional training. Key learning priorities were clinical applications (206 [71.9%]) and risk management (181 [63.1%]). Despite widespread personal use, only 8 physicians (2.6%) recommended LLMs to patients. Notable specialty and demographic variations emerged, with younger physicians showing higher enthusiasm but also elevated legal concerns.This survey study provides insights into physicians' current usage patterns and perspectives on LLMs. Liability concerns appear to be lessened in settings with institutional LLM access. The findings suggest opportunities for medical centers to consider when developing LLM-related policies and educational programs.

This study evaluated the quality of the maternal and child health care in two different models of Primary Health Care. Interviews were carried out by trained personnel with 1200 families randomly selected. Processes of assistance for maternal and child health care were evaluated by Family Health Strategy Teams and traditional health centers. In the evaluation of child health care, the precocity of the first consultation, the regular assessment of growth and development, the recommendations for accident prevention and prophylactic use of iron supplementation and vitamin A had been statistically associated with the model of the health care. Regarding prenatal health care the results showed statistically significant differences between the two models for breastfeeding counseling, nutritional recommendations and cervical preventive screening using Papanicolaou smear. For women health care out of pregnancy period, the results revealed that counseling for breasts auto-examination, preventive screening using Papanicolaou smear in last year and participation in family planning programs were associated with health Primary Health Care model. All the pointed differences had shown better performance of the Family Health Strategy Teams.

BackgroundThe application of large language models (LLMs) in health care holds significant potential for enhancing patient care and advancing medical research. However, the protection of patient privacy remains a critical issue, especially when handling patient health information (PHI).ObjectiveThis scoping review aims to evaluate the adequacy of current approaches and identify areas in need of improvement to ensure robust patient privacy protection in the existing studies about PHI-LLMs within the health care domain.MethodsA search of the literature published from January 1, 2022, to July 20, 2025, was performed on July 20, 2025, using 2 databases (PubMed and Embase). This scoping review focused on the following three research questions: (1) What studies on the development and application of LLMs using PHI currently exist within the health care domain? (2) What patient privacy considerations are addressed in existing PHI-LLMs research, and are these measures sufficient? (3) How can future research on the development and application of LLMs using PHI better protect patient privacy? Studies were included if they focused on the development and application of LLMs within health care using PHI, encompassing activities such as model construction, fine-tuning, optimization, testing, and performance comparison. Eligible literature comprised original research articles written in English. Conversely, studies were excluded if they used publicly available datasets, under the assumption that such data have been adequately deidentified. Additionally, non-English publications, reviews, abstracts, incomplete reports, and preprints were excluded from the review due to the lack of rigorous peer review.ResultsThis study systematically identified 9823 studies on PHI-LLM and included 464 studies published between 2022 and 2025. Among the 464 studies, (1) a small number of studies neglected ethical review (n=45, 9.7%) and patient informed consent (n=148, 31.9%) during the research process, (2) more than a third of the studies (n=178, 38.4%) failed to report whether to implement effective measures to protect PHI, and (3) there was a significant lack of transparency and comprehensive detail in anonymization and deidentification methods.ConclusionsWe propose comprehensive recommendations across 3 phases—study design, implementation, and reporting—to strengthen patient privacy protection and transparency in PHI-LLM. This study emphasizes the urgent need for the development of stricter regulatory frameworks and the adoption of advanced privacy protection technologies to effectively safeguard PHI. It is anticipated that future applications of LLMs in the health care field will achieve a balance between innovation and robust patient privacy protection, thereby enhancing ethical standards and scientific credibility.

The realm of health care is on the cusp of a significant technological leap, courtesy of the advancements in artificial intelligence (AI) language models, but ensuring the ethical design, deployment, and use of these technologies is imperative to truly realize their potential in improving health care delivery and promoting human well-being and safety. Indeed, these models have demonstrated remarkable prowess in generating humanlike text, evidenced by a growing body of research and real-world applications. This capability paves the way for enhanced patient engagement, clinical decision support, and a plethora of other applications that were once considered beyond reach. However, the journey from potential to real-world application is laden with challenges ranging from ensuring reliability and transparency to navigating a complex regulatory landscape. There is still a need for comprehensive evaluation and rigorous validation to ensure that these models are reliable, transparent, and ethically sound. This editorial introduces the new section, titled "AI Language Models in Health Care." This section seeks to create a platform for academics, practitioners, and innovators to share their insights, research findings, and real-world applications of AI language models in health care. The aim is to foster a community that is not only excited about the possibilities but also critically engaged with the ethical, practical, and regulatory challenges that lie ahead.

Application of deep learning and machine learning models to improve healthcare in sub-Saharan Africa: Emerging opportunities, trends and implications

The objective is to provide an overview of the application of large language models (LLMs) in healthcare by employing a bibliometric analysis methodology. We performed a comprehensive search for peer-reviewed English-language articles using PubMed and Web of Science. The selected articles were subsequently clustered and analyzed textually, with a focus on lexical co-occurrences, country-level and inter-author collaborations, and other relevant factors. This textual analysis produced high-level concept maps that illustrate specific terms and their interconnections. Our final sample comprised 371 English-language journal articles. The study revealed a sharp rise in the number of publications related to the application of LLMs in healthcare. However, the development is geographically imbalanced, with a higher concentration of articles originating from developed countries like the United States, Italy, and Germany, which also exhibit strong inter-country collaboration. LLMs are applied across various specialties, with researchers investigating their use in medical education, diagnosis, treatment, administrative reporting, and enhancing doctor-patient communication. Nonetheless, significant concerns persist regarding the risks and ethical implications of LLMs, including the potential for gender and racial bias, as well as the lack of transparency in the training datasets, which can lead to inaccurate or misleading responses. While the application of LLMs in healthcare is promising, the widespread adoption of LLMs in practice requires further improvements in their standardization and accuracy. It is critical to establish clear accountability guidelines, develop a robust regulatory framework, and ensure that training datasets are based on evidence-based sources to minimize risk and ensure ethical and reliable use.

Introduction New models of healthcare have largely focussed on adults, with increasing interest in integrated care. Integrated care models have been identified as a promising solution by policy makers, children and young people (CYP), to target the gaps in healthcare delivery for CYP with on-going conditions. However, there has been limited work on developing and evaluating integrated models of child healthcare. This systematic review and meta-analysis assessed the effects of integrated care on child health, health service use, and healthcare quality for CYP with on-going conditions. Methods Articles were eligible for the review if they 1) reported randomised controlled trials (RCTs), published between 1996 and 2018; 2) evaluated an integrated healthcare intervention designed to improve child health; 3) included CYP (0–18 years) with an on-going health condition; 4) included at least one health-related outcome. Descriptive data was synthesised for all included papers, and data homogeneity allowed further meta-analyses to explore the effects of integrated care interventions compared with usual care, on health-related quality of life (QOL) and number of emergency department visits. Results Twenty-three randomised controlled trials were identified, describing 18 interventions. Studies had medium risk of bias. Improvements were found for quality of care (87% of interventions found a positive effect for intervention) and child health (39% found a positive effect for intervention). The meta-analyses found that integrated care interventions have a positive effect in improving QOL over usual care (n=5 trials; SMD=0.24; 95% CI=0.03, 0.44; p=0.02), but no significant difference across groups for emergency department contacts (n=5 trials;OR=0.82; 95% CI=0.53, 1.26; p=0.37). Conclusion Integrated care interventions for CYP with on-going conditions may deliver improved QOL for children, health, and quality of care. However, no conclusions can be made about the direction or magnitude of the effect for integrated care interventions on emergency department contacts in CYP with on-going conditions. Only 23 RCTs were included in this review, which were of moderate quality, highlighting the need for more robust trials to inform current health service delivery in this area and fully establish the effectiveness of integrated healthcare interventions on CYP outcomes.

Numerous studies examine simulation modelling in healthcare. These studies present a bewildering array of simulation techniques and applications, making it challenging to characterise the literature. The aim of this paper is to provide an overview of the level of activity of simulation modelling in healthcare and the key themes. We performed an umbrella review of systematic literature reviews of simulation modelling in healthcare. Searches were conducted of academic databases (JSTOR, Scopus, PubMed, IEEE, SAGE, ACM, Wiley Online Library, ScienceDirect) and grey literature sources, enhanced by citation searches. The articles were included if they performed a systematic review of simulation modelling techniques in healthcare. After quality assessment of all included articles, data were extracted on numbers of studies included in each review, types of applications, techniques used for simulation modelling, data sources and simulation software. The search strategy yielded a total of 117 potential articles. Following sifting, 37 heterogeneous reviews were included. Most reviews achieved moderate quality rating on a modified AMSTAR (A Measurement Tool used to Assess systematic Reviews) checklist. All the review articles described the types of applications used for simulation modelling; 15 reviews described techniques used for simulation modelling; three reviews described data sources used for simulation modelling; and six reviews described software used for simulation modelling. The remaining reviews either did not report or did not provide enough detail for the data to be extracted. Simulation modelling techniques have been used for a wide range of applications in healthcare, with a variety of software tools and data sources. The number of reviews published in recent years suggest an increased interest in simulation modelling in healthcare.

Aim: This study aimed to examine the conceptual, thematic, and temporal evolution of research applying Andersen’s Behavioural Model (ABM) in healthcare. Methods: A bibliometric analysis was conducted using the Scopus database without time or document type restrictions, focusing on English-language publications. A total of 1223 records (1976-2025) were analysed using co-word and thematic mapping with Bibliometrix and VOSviewer software. Results: A consistent rise in publications was observed after 2008, peaking in 2022. Core study areas regarding healthcare utilisation, mental health, oral health, and access to care were identified by keyword frequency and co-word analyses. Thematic mapping uncovered specialised niches regarding cancer screening, and emerging areas regarding immigrant health, health equity. Cross-cutting concepts regarding oral health and socioeconomic status also emerged as significant connecting topics. Analysis of temporal mapping revealed a shifting in focus from issues related to aging to more recent priorities regarding COVID-19 and health disparities. Conclusion: ABM remains a useful framework for directing equitable, person-centred healthcare research and policy. To improve the model's applicability within global health systems, future research should expand its application through vulnerable populations, structural determinants, qualitative approaches, and emerging public health issues.

ObjectivesGiven the rapidly growing expectations for large language models (LLMs) in healthcare, this study systematically collected perspectives from Korean experts on the potential benefits and risks of LLMs, aiming to promote their safe and effective utilization.MethodsA web-based mini-Delphi survey was conducted from August 27 to October 14, 2024, with 20 selected panelists. The expert questionnaire comprised 84 judgment items across five domains: potential applications, benefits, risks, reliability requirements, and safe usage. These items were developed through a literature review and expert consultation. Participants rated their agreement or perceived importance on a 5-point scale. Items meeting predefined thresholds (content validity ratio ≥0.49, degree of convergence ≤0.50, and degree of consensus ≥0.75) were prioritized.ResultsSeventeen participants (85%) responded to the first round, and 16 participants (80%) completed the second round. Consensus was achieved on several potential applications, benefits, and reliability requirements for the use of LLMs in healthcare. However, significant heterogeneity was found regarding perceptions of associated risks and criteria for safe usage of LLMs. Of the 84 total items, 52 met the criteria for statistical validity, confirming the diversity of expert opinions.ConclusionsExperts reached a consensus on certain aspects of LLM utilization in healthcare. Nonetheless, notable differences remained concerning risks and requirements for safe implementation, highlighting the need for further investigation. This study provides foundational insights to guide future research and inform policy development for the responsible introduction of LLMs into the healthcare field.

This paper explores the emerging role of large language models (LLMs) in healthcare, offering an analysis of their applications and limitations. Attention mechanisms and transformer architectures enable LLMs to perform tasks like extracting clinical information and assisting in diagnostics. We highlight research that demonstrates early application of LLMs in various domains and along the care pathway. With their promise, LLMs pose ethical and practical challenges, including data bias and the need for human oversight. This review serves as a guide for clinicians and researchers, outlining potential healthcare applications – ranging from document translation to clinical decision support – while cautioning about inherent limitations and ethical considerations. The aim of this work is to encourage the knowledgeable use of LLMs in healthcare and drive further study in this important emerging field.

Understanding the low cost business model in healthcare service provision: A comparative case study in Italy