Aug 2026· İstanbul Gelişim Üniversitesi sağlık bilimleri dergisi· pp. 187-196· 0 citations· 24 references
TL;DR
Findings show that clinical decision support systems increase diagnostic accuracy and, through personalized treatment planning, enhance treatment efficacy, and healthcare teams utilizing these AI-supported systems can maximize patient health outcomes.
Abstract
Artificial intelligence (AI) is supporting clinical decision-making processes in healthcare systems, personalizing patient care, and optimizing workflows. The aim of this narrative review is to examine the role of AI technologies in supporting physicians, nurses and physiotherapists, as well as to evaluate the barriers, ethical issues, and educational transformations associated with integration. Articles related to the topic were selected from Google Scholar, PubMed, and Scopus search databases using the keywords “Artificial intelligence, healthcare team, healthcare services, informatics,” without any restrictions on publication year. Findings show that clinical decision support systems increase diagnostic accuracy and, through personalized treatment planning, enhance treatment efficacy. In nursing, AI-supported monitoring systems improve patient safety while reducing administrative burden; in physiotherapy, robotic devices, wearable sensors, and machine learning-based movement analysis support rehabilitation. AI-based health education requires new competencies such as health, data, and human literacy. Key barriers include lack of algorithmic transparency, data privacy concerns, bias, and legal uncertainty regarding accountability. In conclusion, AI functions as “augmented intelligence” that complements rather than replaces healthcare professionals. Effective integration requires transparent infrastructures, clear legal boundaries, workforce training, and human-centered practices. Healthcare teams utilizing these AI-supported systems can maximize patient health outcomes.
Artificial Intelligence (AI) is rapidly transforming healthcare by supporting clinical decision-making, improving patient safety, streamlining documentation, and strengthening nursing education. Nursing professionals are increasingly using AI-enabled technologies such as clinical decision-support systems, predictive analytics, virtual assistants, remote patient monitoring, natural language processing, robotics, and intelligent electronic health records. These technologies can assist nurses in identifying patient deterioration, prioritizing care, reducing medication errors, managing large volumes of clinical information, and developing individualized care plans. In nursing education, AI provides opportunities for adaptive learning, virtual simulation, automated assessment, personalized feedback, and development of clinical reasoning skills. However, successful implementation requires attention to ethical issues, data privacy, algorithmic bias, cybersecurity, professional accountability, technological literacy, and the preservation of human-centered care. Nurses must therefore develop appropriate AI literacy while maintaining critical thinking and clinical judgment. This article discusses the role of AI in nursing practice, its impact on patient care and clinical decision-making, applications in nursing education, advantages, challenges, ethical considerations, and future implications for the nursing profession.
Sumit Padihar, Sunita Joshi, Ratna Parmar et al.· Genetics and Molecular Resea...· 0 citations
Artificial intelligence (AI) is widely regarded as one of the most promising innovations in healthcare, yet its adoption in routine clinical practice remains limited. Only a small proportion of AI applications developed in research settings are successfully integrated into healthcare delivery. Major barriers include poor interoperability with existing health information systems, complex regulatory requirements, limited scientific evidence, and the lack of clear clinical guidelines. Many AI tools have been evaluated through methodologically weak studies, often retrospective and lacking external validation, contributing to skepticism among healthcare professionals. Additional challenges involve healthcare professionals' education and training, algorithm transparency, and the ability of healthcare organizations to effectively incorporate these technologies into clinical workflows. To promote the safe and effective adoption of AI, stronger clinical evidence, structured training programs, and organizational models capable of supporting its implementation are required. Addressing these issues is essential to ensure that AI can deliver meaningful benefits for patients, healthcare professionals, and healthcare systems.
Eugenio Santoro· Recenti progressi in medicin...· 0 citations
Artificial intelligence (AI) is increasingly reshaping nursing practice by supporting clinical decision-making, patient monitoring, documentation, education, workflow management, and patient safety. This integrative literature review critically examines the principal applications, benefits, limitations, and ethical implications of AI in nursing, with emphasis on emergency triage, hospital data management, clinical decision support, humanized care, and safety governance. A structured search of PubMed/MEDLINE and major peer-reviewed journal platforms was conducted, prioritizing literature published from 2020 onward while retaining seminal works required for the theoretical framework. Peer-reviewed original studies, systematic and integrative reviews, umbrella reviews, and authoritative institutional guidance were considered. The evidence indicates that AI can improve risk stratification, early recognition of clinical deterioration, medication-safety processes, documentation quality, and access to simulation-based education. Nevertheless, the literature remains heterogeneous, and many applications lack prospective validation across diverse health systems and populations. Major concerns include algorithmic bias, privacy, opacity, automation bias, alert fatigue, unequal technological access, and unclear accountability. The findings support a human-in-the-loop model in which AI augments rather than replaces nursing judgment. Sustainable adoption therefore requires local validation, continuous performance monitoring, AI literacy, interdisciplinary governance, and explicit protection of patient autonomy and relational care. For Brazil, further multicenter research is needed to evaluate AI under the organizational, demographic, and infrastructural conditions of the Unified Health System. AI should ultimately be assessed not only by technical performance, but by its capacity to strengthen safe, equitable, evidence-based, and person-centered nursing care.
Heloísa de Oliveira Nascimento· Nexus Science Review· 0 citations
Artificial intelligence (AI) has the potential to transform the healthcare industry by improving the quality and efficiency of healthcare services. AI in healthcare can revolutionise various elements of patient care and administrative processes. The primary goal of this investigation was to investigate the uses of AI for the detection, identification and diagnosis of various diseases and health disorders and provide future implications to professionals in the medical and medical informatics fields in AI abilities and competencies. A survey was administered to many doctors working in various healthcare settings across India, and the responses were collected and analysed using statistical methods. According to the report, most healthcare professionals thought AI significantly impacted healthcare quality and effectiveness, with the potential to lower costs and boost patient outcomes. The findings of this study will enhance the ongoing discussions on the incorporation of AI in healthcare and offer suggestions for long-term progress in healthcare. The results of this study add to the expanding body of knowledge about how AI is affecting healthcare and shed light on the viewpoints of healthcare workers, which can help policymakers and decision-makers in the healthcare sector make decisions that will promote sustainable development.
Ikshita Rao, J. Shivarama, A. Sahu· Journal of Health Management· 0 citations
Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions and has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.
Mohammed Majid Bakhsh, Emran Hossain, M. K. K. Rony et al.· Personalized Medicine· 0 citations
This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer to highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics.
Irwan Bastian, Aqilla Rahman Musyaffa, Lukman Nulhakim et al.· IAES International Journal o...· 0 citations
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