Digital health tools can improve health care access and outcomes for individuals with limited access to health care, particularly those residing in rural areas. This scoping review examines the existing literature on using digital tools in patients with limited access to health care in rural areas. It assesses their effectiveness in improving health outcomes. The review adopts a comprehensive search strategy to identify relevant studies from electronic databases, and the selected studies are analyzed descriptively. The findings highlight the advantages and barriers of digital health interventions in rural populations. The advantages include increased access to health care practitioners through teleconsultations, improved health care outcomes through remote monitoring, better disease management through mobile health applications and wearable devices, and enhanced access to specialized care and preventive programs. However, limited internet connectivity and a lack of familiarity with digital tools are barriers that must be addressed to ensure equitable access to digital health interventions in rural areas. Overall, digital tools improve health outcomes for individuals with limited health care access in rural areas.

Background Remote patient monitoring (RPM), or telemonitoring, offers ways for health care practitioners to gather real-time information on the physiological conditions of patients. As telemedicine, and thus telemonitoring, is becoming increasingly relevant in today's society, understanding the practitioners' opinions is crucial. This systematic review evaluates the perspectives and experiences of health care practitioners with telemonitoring technologies. Methods A database search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for the selection of articles measuring health care practitioners' perspectives and experiences with RPM technologies published between 2017 and 2021. Only articles written in English were included. No statistical analysis was performed and thus this is a qualitative review. Results A total of 1605 studies were identified after the initial search. After applying the inclusion and exclusion criteria of this review's authors, 13 articles were included in this review. In all, 2351 practitioners' perspectives and experience utilizing RPM technology in a variety of medical specialties were evaluated through close- and open-ended surveys. Recurring themes emerged for both the benefits and challenges. Common benefits included continuous monitoring of patients to provide prompt care, improvement of patient self-care, efficient communication, increased patient confidence, visualization of health trends, and greater patient education. Challenges comprised increased workload, higher patient anxiety, data inaccuracy, disorienting technology, financial issues, and privacy concerns. Conclusion Health care practitioners generally believe that RPM is feasible for application. Additionally, there is a consensus that telemonitoring strategies will become increasingly relevant. However, there are still drawbacks to the technology that need to be considered.

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.

Introduction: As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Five electronic databases were searched in October 2023. Eligible studies included surgery-related articles that focused on large language models and contained adequate ethical discussion. Study details, including specialty and ethical concerns, were collected. Results: The literature search yielded 1179 articles, with 53 meeting the inclusion criteria. Plastic surgery, orthopedic surgery, and neurosurgery were the most represented surgical specialties. Autonomy was the most explicitly cited ethical principle. The most frequently discussed ethical concern was accuracy (n = 45, 84.9%), followed by bias, patient confidentiality, and responsibility. Conclusion: The ethical implications of using large language models in surgery are complex and evolving. The integration of these models into surgery necessitates continuous ethical discourse to ensure responsible and ethical use, balancing technological advancement with human dignity and safety.

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles. Only CDSSs powered by AI targeted to physicians and tested in real clinical PHC settings were included. From a total of 421 articles, 6 met our criteria. We found AI-CDSSs from the US, Netherlands, Spain, and China whose primary tasks included diagnosis support, management and treatment recommendations, and complication prediction. Secondary objectives included lessening physician work burden and reducing healthcare costs. While promising, the outcomes were hindered by physicians’ perceptions and cultural settings. This study underscores the potential of AI-CDSSs in improving clinical management, patient satisfaction, and safety while reducing physician workload. However, further work is needed to explore the broad spectrum of applications that the new AI-CDSSs have in several PHC real clinical settings and measure their clinical outcomes.

Background: Addressing language barriers through accurate interpretation is crucial for providing quality care and establishing trust. While the ability of artificial intelligence (AI) to translate medical documentation has been studied, its role for patient-provider communication is less explored. This review evaluates AI’s effectiveness in clinical translation by assessing accuracy, usability, satisfaction, and feedback on its use. Methods: A systematic search was conducted on July 11, 2024, across Cumulated Index in Nursing and Allied Health Literature (CINAHL), Institute of Electrical and Electronics Engineers (IEEE) Xplore, PubMed, Scopus, Web of Science, and Google Scholar. Inclusion criteria required AI to translate clinical information for a real or theoretical consultation. Exclusion criteria included reviews, correspondence, educational materials, non-peer-reviewed or retracted reports, non-English translations, pre-2016 publications, and reports on sign language or patient education. Search strings representing AI, language interpretation, and healthcare were used. Two investigators independently conducted the screening, extraction, synthesis of results, and bias assessments using Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I), Mixed Methods Appraisal Tool (MMAT), and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. A third investigator resolved conflicts. Results: Of 1,095 reports, 9 studies were analyzed, evaluating AI translation platforms Google Translate, Microsoft Translator, Apple iTranslate, AwezaMed, Pocketalk W, and the Asynchronous Telepsychiatry (ATP) App. Investigations occurred in the US, France, Switzerland, and South Africa, with publications from 2019–2024. AI medical translation shows promise, typically providing accurate translations for brief communications in limited languages, though human translation is often necessary. Accuracy scores ranged from 83–97.8% when translating from English, and 36–76% when translating to English. Usability scores were 76.7–96.7%. Patients were more satisfied than clinicians, with 84–96.6% and 53.8–86.7% satisfied, respectively. Clinicians were hesitant to use AI due to questions of respect, quality, reliability, and misunderstanding. AI is being used as a last-resort option, to assist fluent, non-certified providers and lay interpreters, and for brief communications. Conclusions: Limitations include few languages tested, unidirectional translation, simulation, and evolving translation tools. AI shows promise in clinical translation, but the complexity of medical consultations requires a balanced approach combining AI and human translation services for quality care.