ReviewDigital health
Harnessing digital health technologies and real-world evidence to enhance clinical research and patient outcomes.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Artificial intelligence (AI)-aided clinical data management: Applications, human-in-the-loop workflows, and regulatory considerations.Global health & medicine · 2026Review
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The intersection of digital health technologies (DHT) and real-world evidence (RWE) is redefining the landscape of clinical research and regulatory science, offering unique opportunities to improve drug development, patient monitoring, and evidence-based decision-making. The extensive use of digital technologies has facilitated continuous, real-time health data collection, while RWE helps bridge the gap between controlled clinical trials and real-world patient experiences. As healthcare systems progressively integrate these various data sources, they are reshaping regulatory approval processes and enhancing post-market surveillance of therapies. Furthermore, advancements in the integration of artificial intelligence and the application of pharmacogenomics results in more effective, efficient, and personalized healthcare delivery. Nonetheless, these developments are met with critical challenges, and the lack of standardized global regulatory frameworks creates disparities in how this data is utilized across jurisdictions. Addressing these limitations through robust validation frameworks, harmonized regulatory policies, and interdisciplinary collaboration is essential to realizing the complete potential of DHT and RWE in precision medicine applications. This review examines the opportunities, challenges, and future directions of merging DHT and RWE to enhance precision medicine, emphasizing the importance of collaboration among researchers, regulators, and healthcare providers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.