Aug 2026· International Journal of Clinical Pharmacy· 0 citations· 28 references
Medicine
TL;DR
The proposed framework conceptualizes AI as a relevance-structuring, clinician-governed decision-support layer rather than an autonomous decision-maker rather than an autonomous decision-maker and provides a conceptual foundation for future development, implementation, and evaluation of AI-supported pharmacotherapy systems.
Abstract
INTRODUCTION
Pharmacotherapy optimization in multimorbid patients is increasingly complex due to polypharmacy, fragmented data, expanding electronic health records, and workforce constraints. Conventional clinical decision support systems remain largely rule-based and often fail to adequately incorporate patient-specific context. While artificial intelligence offers new opportunities, stand-alone models remain insufficiently reliable for high-risk pharmacotherapy decision support.
Aim
To develop a relevance-driven, clinician-supervised hybrid AI framework for pharmacotherapy optimization.
Method
Using a design science-informed approach, an interdisciplinary research group developed a conceptual framework for AI-supported pharmacotherapy optimization. Framework development was informed by prior feasibility work, published literature, clinical practice requirements, and iterative interdisciplinary discussions. Hybrid AI was defined as the combination of retrieval-augmented generation, deterministic safety rules, and large language model reasoning.
Results
Seven design principles were identified, including decomposition of clinical activities, relevance-based prioritization, hybrid reasoning under clinician oversight, integration of patient goals, transparency of evidence sources, longitudinal optimization within a governed closed loop, and evaluation as a design requirement. These principles informed a conceptual architecture integrating structured clinical data, patient preferences, longitudinal patient information, and evidence retrieval within a clinician-governed decision-support framework.
Conclusion
The proposed framework conceptualizes AI as a relevance-structuring, clinician-governed decision-support layer rather than an autonomous decision-maker. By combining hybrid reasoning, patient-specific context, and professional oversight, it provides a conceptual foundation for future development, implementation, and evaluation of AI-supported pharmacotherapy systems.
The management of pharmacotherapy in patients with multimorbidity and polypharmacy is a difficult task in routine clinical practice. Clinicians must consider diagnoses, prescribed drugs, contraindications, drug-drug interactions, renal and hepatic function, laboratory values, dose limits and individual risk factors. Clinical decision support systems in traditional settings are based on static rules, and hard to update. Standalone large language models are capable of processing clinical language but their outputs may be unsupported by evidence, incomplete or out of date. In this study, we developed and evaluated a hybrid multi-agent GraphRAG framework for personalised pharmacotherapy safety verification.
We developed a system that integrates graph based retrieval with large language models. The framework was composed of three parts: (1) a schema for a pharmacotherapy knowledge graph that encoded indications, contraindications, interactions, laboratory thresholds, dose limitations and relevant patient conditions; (2) an Extractor–Critic workflow that transformed unstructured medical text into graph elements with quality checks prior to their incorporation; and (3) a retrieval and re-ranking module that leveraged semantic search, graph traversal and safety-directed scoring. The system was evaluated on 12 benchmark cases extraction and 30 clinical safety questions answering. Comparison of conventional RAG and GraphRAG was done using linear mixed effects models, permutation testing, and paired case-level analysis.
The multi-agent extraction workflow beat the zero-shot baseline. Strict F1 improved by 0.156 points (
p
< 0.01), mainly due to higher ecall (+0.172,
p
< 0.001). For clinical question answering, we found that GraphRAG outperformed conventional RAG in terms of medical accuracy (0.545 vs. 0.443, absolute difference of 0.102,
p
= 0.04). GraphRAG provided the better answer 66.7% of the time. The advantage was greatest where safety was dependent on contra-indications, laboratory cut-offs or patient specific factors. Smaller models performed as well as the larger models with the same GraphRAG configuration.
The evaluation revealed that the integration of LLM-based extraction with a pharmacotherapy knowledge graph enhanced safety-oriented clinical decision support. GraphRAG provided more accurate and traceable answers than traditional RAG. Routine adoption will require validation on larger graphs, a wider range of datasets and real clinical populations, but this approach may be appropriate for cost sensitive and privacy conscious workflows.
V. Ryzhenko, Bogdan Burlaka, Igor Belenichev et al.· Frontiers in Medicine· 0 citations
INTRODUCTION
Health economic models (HEMs) provide a solid foundation for reimbursement policy decisions that shape patient access to new treatments and the allocation of scarce healthcare resources. Model development is labor-intensive and time-consuming, often requiring months of expert work. Recent advances in large language models (LLMs) prompted interest in whether artificial intelligence can support or partially automate this process, but the evidence base remains scattered and has not been mapped against the modeling workflow.
AREAS COVERED
This review examines current applications of LLMs to health economic modeling. Five proof-of-concept studies are included and mapped to an eight-stage workflow adapted from the ISPOR-SMDM Modeling Good Research Practices framework and discussed in terms of reproducibility, validation, adaptability, and technology readiness. Published work addressed model parameterization, model implementation, reporting and quality assessment, and local adaptation, while research question design, model conceptualization, uncertainty analysis, and model validation remained unaddressed.
EXPERT COMMENTARY
The evidence supports cautious optimism. Near-term gains are augmenting human modelers on decomposed, verifiable sub-tasks rather than pursuing autonomous end-to-end modeling, which remains distant given current reliability levels and the iterative, collaborative nature of model development.
Attila Imre, B. Németh, Á. Jóźwiak et al.· Expert review of pharmacoeco...· 0 citations
Multimodal artificial intelligence (AI) agents are emerging in healthcare as systems that integrate heterogeneous clinical data, foundation models (FMs), tools, and agentic workflows, but their applications and translational readiness remain unclear. We conducted a scoping review of 37 peer-reviewed studies published between January 2022 and June 2025. Included studies covered clinical decision support (
N
= 17), clinical documentation and report generation (
N
= 3), clinical monitoring and health management (
N
= 13), and medical education and training (
N
= 4). We synthesized modality combinations and fusion strategies, FM utilization and agent architectures, tool integration, agent capabilities, and evaluation practices. Current systems were predominantly text-centric, frequently used closed-source FMs, and remained concentrated in prototype or early technical evaluation stages. Safety, fairness, prospective outcome-based validation, and real-world deployment evidence were limited. These findings suggest that multimodal AI agents are best interpreted as emerging augmentative systems requiring stronger evaluation before clinical translation.
Kai Yu, Shuang Zhou, Yu Hou et al.· npj Digital Medicine· 1 citation
INTRODUCTION
Artificial intelligence (AI) is increasingly recognized as a transformative paradigm within transplantation medicine, offering advanced computational approaches capable of integrating heterogeneous clinical, biological, imaging, and molecular datasets to improve predictive accuracy and decision-making. Liver transplantation represents a uniquely complex clinical domain characterized by high-dimensional data, nonlinear interactions among risk factors, and critical time-dependent decision processes, thereby providing an ideal context for AI-enabled analytics.
METHODS
The objective of this systematic review was to critically synthesize current evidence regarding AI applications in liver transplantation, with emphasis on data modalities, algorithmic methodologies, targeted clinical outcomes, validation strategies, and reported performance metrics. A comprehensive search of MEDLINE, Scopus, and the Cochrane Library identified 1045 records following duplicate removal and automated filtering.
RESULTS
After screening and eligibility assessment, 65 studies met the inclusion criteria. Laboratory data represented the most frequently utilized input (n = 35), followed by clinical (n = 28), demographic (n = 19), imaging (n = 13), and genetic or molecular data (n = 5), with several studies employing multimodal integration. Deep-learning architectures and neural network-based approaches predominated, with additional contributions from ensemble learning methods and conventional machine-learning algorithms. Across multiple clinical domains-including diagnostic classification, prognostic modeling, graft survival prediction, and treatment optimization-AI systems demonstrated high predictive performance, frequently surpassing traditional risk stratification tools such as model for end-stage liver disease and Survival Outcomes Following Liver Transplantation scores. Imaging-based models achieved particularly strong segmentation accuracy, whereas genomic and molecular approaches demonstrated excellent discriminative capability in oncologic and graft-related outcomes.
CONCLUSIONS
Despite these promising findings, significant methodological limitations persist, including data heterogeneity, insufficient external validation, risk of bias, and challenges related to interpretability, fairness, and ethical deployment. Overall, AI represents a highly promising adjunct to clinical decision-making in liver transplantation; however, robust prospective validation, standardized reporting frameworks, and clinically interpretable implementations remain necessary prior to widespread adoption.
Panagiotis Boutos, James L. Rogers, Efthymia Kouvela et al.· Journal of Surgical Research· 0 citations
Artificial Intelligence (AI) is rapidly transforming healthcare sector by improving diagnostic accuracy, enhancing drug discovery and pharmaceutical research, and advancing electronic prescription processing (e-prescribing; eRx), although its adoption in developing contexts remains limited. This study the role of artificial intelligence in transforming clinical practice and biomedical research: a review of opportunities and challenges presents systematic review of 20 peer-reviewed articles examining the role of AI in clinical practice and biomedical research. The key findings show that AI significantly improves diagnostic precision, supports personalized medicine, enhances real-time patient management system, and optimizes medication management through intelligent e-prescribing systems with accuracy of perception of drugs for each patient. Additionally, AI contributes to faster and more efficient biomedical research processes. However, challenges such as inadequate infrastructure, high implementation costs, data privacy concerns, and limited technical expertise persist, particularly in developing country. The review concludes that effective and sustainable AI integration in healthcare requires context-specific strategies, supportive policy frameworks, increased investment in digital infrastructure, and capacity building among healthcare professionals.
Muhammad Sadisu Isah, Musbahu Salisu, Eli Adama Jiya· Journal of Basics and Applie...· 0 citations
Introduction: The digital transformation of healthcare is accelerating, driven by unprecedented advancements in Artificial Intelligence (AI). From large language models (LLMs) to biomolecular structure prediction, AI is redefining modern diagnostic and therapeutic standards.
Aim: This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation.
Materials and Methods: A structured analysis was conducted utilizing a hybrid approach that combines a multi-decade bibliometric trend perspective with a detailed synthesis of 21 landmark publications, clinical trials, and meta-analyses from high-impact journals.
Results: AI demonstrates expert-level performance in medical knowledge retrieval and spatiotemporal diagnostics. AlphaFold 3 has revolutionized computational therapeutics through all-atom biomolecular interaction prediction, while ambient AI scribes significantly reduce physician burnout by automating clinical documentation workflows. However, data-driven "hallucinations" in LLMs and the inherent "black box" nature of deep learning architectures remain critical barriers to autonomous deployment.
Conclusions: AI is successfully transitioning from an isolated research tool into an essential clinical "co-pilot." Achieving its full potential in Medicine 4.0 requires robust frameworks for algorithmic explainability, global dataset diversification, and a strategic synergy between machine precision and human clinical judgment.
Aleksandra Stańczyk, Kinga Haduch, Zuzanna Michalska et al.· International Journal of Inn...· 0 citations