Advances in Generating Real-World Evidence from Real-World Data Using Artificial Intelligence

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: 28 December 2025 | Viewed by 2673

Special Issue Editor


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Guest Editor
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, USA
Interests: intelligent data aggregation; predictive analytics; the conduct of clinical trials; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Real-world evidence (RWE) consists of evidence on patient care and health outcomes that has been developed from real-world data (RWD) generated in routine clinical settings. Its uses span clinical and regulatory decision-making, health technology assessment, clinical trial design, assessing the burden of illness, evaluating market potential for new products, etc. The wide usage of the internet, social media, wearable sensors, mobile devices, electronic billing, disease and product registries, electronic health records and other technology-driven services, together with increased capacity in data storage, have led to the rapid generation and availability of vast amounts of RWD. The increasing accessibility of RWD and the fast development of artificial intelligence (AI) and machine learning (ML) techniques, together with rising costs and the recognized limitations of traditional trials, has spurred great interest in the use of RWD to enhance the efficiency of clinical research and discoveries, and bridge the evidence gap between clinical research and practice. Modern AI approaches have significant potential in generating RWE from complex multimodal data. However, the generation of high-quality RWE using AI faces a spectrum of challenges such as data quality, heterogeneity and completeness, selection bias and generalizability, temporal drifts of longitudinal data, ethical and privacy concerns, regulatory acceptance and validation, and explainability.

This Special Issue will invite original articles, reviews and commentaries representing the best practices and current advances in AI applications for RWE generation, as well as discussions on approaches to address existing challenges.

Prof. Dr. Joseph Finkelstein
Guest Editor

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Keywords

  • real-world evidence
  • artificial intelligence
  • healthcare

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Published Papers (1 paper)

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Review

13 pages, 352 KiB  
Review
Bridging the Gap: From AI Success in Clinical Trials to Real-World Healthcare Implementation—A Narrative Review
by Rabie Adel El Arab, Mohammad S. Abu-Mahfouz, Fuad H. Abuadas, Husam Alzghoul, Mohammed Almari, Ahmad Ghannam and Mohamed Mahmoud Seweid
Healthcare 2025, 13(7), 701; https://doi.org/10.3390/healthcare13070701 - 22 Mar 2025
Viewed by 1888
Abstract
Background: Artificial intelligence (AI) has demonstrated remarkable diagnostic accuracy in controlled clinical trials, sometimes rivaling or even surpassing experienced clinicians. However, AI’s real-world effectiveness is frequently diminished when applied to diverse clinical settings, owing to methodological shortcomings, limited multicenter studies, and insufficient real-world [...] Read more.
Background: Artificial intelligence (AI) has demonstrated remarkable diagnostic accuracy in controlled clinical trials, sometimes rivaling or even surpassing experienced clinicians. However, AI’s real-world effectiveness is frequently diminished when applied to diverse clinical settings, owing to methodological shortcomings, limited multicenter studies, and insufficient real-world validations. Objective: This narrative review critically examines the discrepancy between AI’s robust performance in clinical trials and its inconsistent real-world implementation. Our goal is to synthesize methodological, ethical, and operational challenges impeding AI integration and propose a comprehensive framework to bridge this gap. Methods: We conducted a thematic synthesis of peer-reviewed studies from the PubMed, IEEE Xplore, and Scopus databases, targeting studies from 2014 to 2024. Included studies addressed diagnostic, therapeutic, or operational AI applications and related implementation challenges in healthcare. Non-peer-reviewed articles and studies without rigorous analysis were excluded. Results: Our synthesis identified key barriers to AI’s real-world deployment, including algorithmic bias from homogeneous datasets, workflow misalignment, increased clinician workload, and ethical concerns surrounding transparency, accountability, and data privacy. Additionally, scalability remains a challenge due to interoperability issues, insufficient methodological rigor, and inconsistent reporting standards. To address these challenges, we introduce the AI Healthcare Integration Framework (AI-HIF), a structured model incorporating theoretical and operational strategies for responsible AI implementation in healthcare. Conclusions: Translating AI from controlled environments to real-world clinical practice necessitates a multifaceted, interdisciplinary approach. Future research should prioritize large-scale pragmatic trials and observational studies to empirically validate the proposed AI Healthcare Integration Framework (AI-HIF) in diverse, real-world healthcare contexts. Full article
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