Artificial Intelligence-Driven Clinical Decision Support in Pakistan: Implications for Primary Care Practice

[Artificial intelligence and healthcare: why is it difficult to move from the research phase to clinical practice?]

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.

Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review

Background/Objectives: Artificial intelligence (AI) is increasingly applied across spinal pain care, by improving diagnostic accuracy, optimizing clinical workflow, and advancing translational research. However, AI’s role and its integration into rural spine care face challenges. Therefore, the current narrative review synthesizes the role of AI in rural spine care, spanning diagnosis, clinical decision support, imaging, natural-language processing, and remote monitoring. Methods: We searched the peer-reviewed literature on AI in spinal pain care across six databases from inception to June 2026. Results: In the United States, AI models have shown promise in strengthening clinical decision support that assists clinicians in identifying spinal pain disorders and providing evidence-based treatment recommendations; however, most were developed and validated in urban healthcare settings. Additionally, evidence on external validation, dataset representativeness, robustness to incomplete or low-quality data, interoperability, prospective clinical utility, and implementation feasibility in rural spine care remains limited. Therefore, we propose a translational framework in which de-identified data from rural and urban healthcare settings are aggregated, harmonized, and used to develop a multimodal AI model. AI models further require rigorous technical and external validation, prospective validation in rural settings, explainability, and continuous post-implementation monitoring. Despite its benefits, key limitations, including data scarcity and algorithmic bias, are also highlighted. Conclusions: AI has emerged as a promising tool for strengthening clinical decision support in spinal pain disorders in rural settings. The most realistic role of AI in rural settings is not to replace specialist expertise, but to act as one component of a multidisciplinary care team.

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

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.

Artificial Intelligence in Hospital-Based Clinical Decision-Making: A Qualitative Study on Physicians And Nurses Experiences with Clinical Decision Support Systems

Large Language Models and Medical AI Systems for Healthcare Diagnosis: A Systematic Review

Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.

Bridging gaps in health artificial intelligence: challenges in MDPI research articles

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.