ReviewJournal of medical systems2025
Integrating mHealth Innovations into Decentralized Oncology Trials.
Review in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Opportunities and Risks of Technology Convergence in Precision Health.Blockchain in healthcare today · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of mobile health (mHealth) technologies into decentralized clinical trials (DCTs) may represent a paradigm shift in oncology research, offering innovative solutions to longstanding challenges in clinical trial design and execution. mHealth tools, including wearable biosensors, telemedicine platforms, and artificial intelligence (AI)-driven analytics, have the potential to enable real-time patient monitoring, support participant engagement, and facilitate remote data collection. Also, these advancements have the potential to improve recruitment rates, optimize treatment adherence, and ensure more equitable access to clinical trials, particularly for patients in underserved regions. Moreover, precision oncology approaches leveraging mHealth data may contribute to personalized treatment strategies that improve patient outcomes. However, regulatory complexities, data privacy concerns, and technological disparities remain critical challenges that must be addressed to ensure the widespread adoption of mHealth-enabled DCTs. Future advancements in AI, blockchain technology, and remote diagnostic tools may further enhance the scalability, efficiency, and inclusivity of these trials. By embracing these innovations, oncology research can transition toward a more patient-centric, data-driven paradigm that has the potential to accelerates drug development and enhances clinical decision-making. This narrative review explores the potentially transformative role of mHealth technologies in DCTs within oncology.
Indexed as
Identifiers
40779078What Socratic 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 Socratic graph.