2025· Journal of Cognitive Human-Computer Interaction· 0 citations· 7 references
TL;DR
The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation.
Abstract
Artificial intelligence (AI) is becoming an important technology in modern healthcare because of its ability to analyze large volumes of clinical, biomedical, and patient-generated data. AI-based systems are being applied in disease diagnosis, medical imaging, drug discovery, personalized medicine, clinical decision support, remote monitoring, and healthcare administration. This review examines recent trends in the use of AI in healthcare by analyzing six influential studies published between 2017 and 2024. The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care. However, challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation. The review concludes that AI should be implemented as a supportive tool under appropriate clinical supervision, ethical standards, and regulatory frameworks.
Background: Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare, significantly influencing clinical decision-making processes. AI-driven systems, including machine learning algorithms, deep learning models, natural language processing, and clinical decision support systems, have demonstrated the ability to analyze vast amounts of healthcare data, identify patterns, predict outcomes, and support evidence-based clinical decisions. As healthcare systems face increasing patient complexity, workforce shortages, and demands for improved quality of care, AI offers opportunities to enhance diagnostic accuracy, treatment planning, risk prediction, and healthcare efficiency. However, concerns regarding algorithm transparency, ethical accountability, data privacy, bias, and professional acceptance continue to challenge its widespread adoption.
Objectives
To systematically review the evidence regarding the role of artificial intelligence in clinical decision-making across healthcare settings.
To identify the benefits and effectiveness of AI-supported clinical decision-making systems in improving patient outcomes.
To examine challenges, ethical considerations, and barriers associated with AI implementation in clinical decision support.
To evaluate implications for healthcare professionals, particularly nurses and physicians, in AI-assisted clinical practice.
Methods: This systematic review was conducted according to Joanna Briggs Institute (JBI) methodology and reported following PRISMA 2020 guidelines. Electronic databases including PubMed, Scopus, Web of Science, CINAHL, ScienceDirect, and Google Scholar were searched for peer-reviewed studies published between 2019 and 2025. The review question was structured using the PICOT framework. Methodological quality was assessed using appropriate JBI Critical Appraisal Tools. Due to heterogeneity among study designs and outcomes, findings were synthesized narratively.
Results: Thirty-five studies met the inclusion criteria. Evidence demonstrated that AI-assisted clinical decision-making significantly improved diagnostic accuracy, risk stratification, treatment planning, predictive analytics, medication management, and workflow efficiency. AI systems were particularly effective in radiology, oncology, cardiology, intensive care, and chronic disease management. However, challenges related to explainability, algorithmic bias, legal liability, data privacy, workforce adaptation, and ethical governance were consistently reported.
Conclusion: Artificial intelligence has substantial potential to strengthen clinical decision-making and improve healthcare outcomes. Successful integration requires robust regulatory frameworks, transparent algorithms, ethical governance, clinician training, and preservation of patient-centered care principles. AI should function as a supportive tool that enhances rather than replaces human clinical judgment.
Arun James, Dr. Jomon Thomas, Dr. Deepika Verma et al.· PAIN, JOINTS, SPINE· 0 citations
INTRODUCTION
The use of Artificial Intelligence (AI), especially Machine learning (ML) and Deep learning (DL), has led to a major shift in medical diagnosis. AI can assist medical professionals in medical diagnosis by its unique ability to analyze complex data from multiple sources, including medical images, gene sequences, and Electronic Health Records (EHRs).
AREAS COVERED
Its application in other clinical processes, such as risk classification, diagnostic workflows, and disease risk prediction from patient symptoms, can also speed up diagnosis, reduce costs, and improve diagnostic outcomes.
EXPERT OPINION
However, its effective use in the clinic necessitates addressing concerns about data privacy, rigorous validation, and the development of methods to reduce bias caused by medical data. By addressing these limitations, AI can be very effective in increasing medical professionals' knowledge and, consequently, improving patient outcomes.
Aghdas Ramezani, Marzieh Bagheri, Fatemeh Mahmoudian et al.· Expert Review of Molecular D...· 0 citations
In the healthcare industry, AI and computer-based systems promise to advance patient care, improve diagnostic precision, and facilitate improved clinical decision-making. In this review, the use of cutting-edge AI tools like machine learning, deep learning, and natural language processing, along with telemedicine systems, clinical decision support systems, electronic health records, and lab automation systems, is explored. Early identification of disease, optimized treatment, evidence-based processes, real-time analytics, and efficient data management can be performed using artificial intelligence techniques. Currently, medical imaging, early diagnosis, drug development, personalized medicine, robotic-assisted surgery, and predictive analytics are important applications. Combining the technologies of Artificial Intelligence, Cloud Computing, and Internet of Medical Things allows for intelligent service in healthcare and constant monitoring of patients. It still, however, has challenges to address, such as the lack of skilled professionals, the cost of implementation, privacy and security issues, connectivity, and data quality. There's also an ethical concern because there's a lack of transparency and algorithmic prejudice. These challenges must be tackled by bridging the gap between disciplines, implementing robust regulations, and continually advancing technology to ensure the successful application of AI in healthcare. The technology could revolutionize the industry.
Fawaz H. Alanazi· Journal of Intelligent Decis...· 0 citations
Large AI models are reshaping biomedical research and healthcare delivery by promoting more intelligent, data-driven, and integrated approaches by demonstrating superior capabilities in knowledge representation, reasoning, and cross-domain learning.
Boyuan Fang· Applied and Computational En...· 0 citations
It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve.
Vinit Kumar Ramawat, G.PRABHAKARAN, Pinki Das et al.· International journal of com...· 0 citations
Artificial intelligence (AI) has transformed the modern medical landscape. Medical devices benefit from innovative data collection, utilization of machine learning, predictive analytics, and intelligent decision support systems in their design and implementation. Applications include medical devices for diagnosis, screening, patient monitoring, tailored care, robotic procedures, and streamlining hospital processes. Despite these benefits, the development of new AI-powered devices presents many new regulatory, legal, ethical and cyber security challenges to be addressed. This research paper provides a review of the recent literature regarding advances in AI-powered medical devices, including their mechanisms of action, clinical applications, regulation, and regulation challenges. A qualitative narrative review was performed synthesizing the content of 15 carefully selected papers that fit the inclusion criteria. An analysis revealed that AI-powered medical devices have improved accuracy in diagnosis, enabled precision care, advanced patient monitoring, and optimized component manufacturing. Our review further identified a future where evolving regulation frameworks, such as risk-based control strategies, as well as increased explainability, interpretability, infrastructure protection, and compliance will help to facilitate widespread AI adoption in medicine.
C. Liakou, Marios Papadakis, Markos Plytas· International Journal of Med...· 0 citations