Aug 2026· Personalized Medicine· pp.
1-28
· 0 citations· 79 references
Medicine
TL;DR
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.
Abstract
This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.
Artificial intelligence (AI) has become an integral component of clinical decision support systems, improving diagnostic accuracy, risk prediction, treatment planning, and healthcare resource management. But, the "black box" nature of many successful machine learning models has brought up concerns around trust and accountability, fairness and regulatory acceptance in the clinical setting. To address these challenges, Explanatory Artificial Intelligence (XAI) has become a promising method that aims to provide the explainability of the results predicted by the models without compromising the performance of the analysis. The aim of this narrative review is to provide an overview of the evolving image of XAI as a tool to create trustworthy clinical decision support systems and how it can be performed based on the concepts of transparency, interpretability and moral decision-making. It provides a summary of the current literature on essential explainability techniques, how they can be applied to different health care-related problems, and how they help increase trust and transparency in health care decision making. It also addresses human-AI collaboration, model validation, bias mitigation, privacy protection and governance frameworks to enable responsible use of AI. The new emerging developments, such as federated learning, multimodal explainable models, causal reasoning, and generative AI, are also discussed to emphasize future opportunities for clinically reliable and scalable intelligent healthcare systems. The review concludes that explainability is no longer a choice of technical attribute but rather a fundamental component to the use of AI in everyday clinical practice. For safe, equitable and trusted clinical decision support to benefit everyone in the health care sector, transparency, accountability and ethical governance will play a pivotal role.
Rakesh Venuturumilli, Amoli Singh, Hemanshi Dhaduk et al.· European Journal of Prosthod...· 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
INTRODUCTION
Artificial intelligence (AI) holds tremendous promise to improve clinical decision-making across diagnosis, risk assessment, and patient care. However, most prior work has focused on model development in controlled settings with limited evidence on real-world implementation. The Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI), led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation, was established to evaluate and support integration of AI tools into routine clinical practice. This article summarizes early insights from AIM-HI-funded projects to inform real-world AI implementation.
METHODS
AIM-HI funded 5 projects through a national, multistage review process using a structured scoring rubric. Projects addressed sepsis management, venous thromboembolism risk assessment, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction across diverse health care settings. The authors synthesized cross-project findings related to implementation processes, challenges, and lessons learned.
RESULTS
Real-world AI deployment was feasible across varied clinical environments. Common challenges included electronic health record integration, data complexity, regulatory requirements, and variation in clinical workflows. Other common themes included stakeholder engagement, local adaptation, quality assurance, and performance monitoring.
DISCUSSION
Findings highlight that implementation success depends on thoughtful integration into clinical environments, strong partnerships with stakeholders, as well as sustained evaluation and monitoring.
CONCLUSION
Effective AI adoption in health care requires careful integration, stakeholder alignment, and ongoing evaluation. Initiatives such as AIM-HI are essential for building the evidence base for scalable, real-world AI implementation.
I. Ergas, H. Clancy, Thomas Wang et al.· The Permanente Journal· 0 citations
This research proposes a framework for the sustainable integration of AI-CDSS into Pakistan's healthcare system, with a focus on scaling solutions to primary care environments such as Basic Health Units and rural health centers.
Huma Ahsan, F. Qazi, Amna Amir Jalal et al.· Liaquat National Journal of...· 0 citations
This narrative review synthesises clinically relevant evidence on AI-enabled early diagnostics, precision therapeutic pathways, and the principal structural barriers to responsible adoption to find AI is best positioned as an augmentation of clinical judgement rather than a replacement for it.
M. Ahmad, Muhammad Ibrahim Ahmed, Ahmad Sajjad Ashraf· Journal of Advances in Medic...· 0 citations
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.
Ramazan Demirer· İstanbul Gelişim Üniversites...· 0 citations
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