ReviewFrontiers in medicine2021
Data Integration Challenges for Machine Learning in Precision Medicine.
Review in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 2 of them syntheses that pooled it.
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
68 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review.The oncologist · 2025Pooled it
- Advancing Health Care With Digital Twins: Meta-Review of Applications and Implementation Challenges.Journal of medical Internet research · 2025Pooled it
- X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology.ESMO real world data and digital oncology · 2026Article
- Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.Clinical and experimental nephrology · 2026Review
- Article
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Toward a Common Set of Interface Requirements for Genomic Data Management: Scoping Review.Journal of medical Internet research · 2026Article
- The adaptive engagement framework: enhancing banking customer experience through AI-powered invisible marketing.Scientific reports · 2026Article
- Data-driven machine learning modelling in wire EDM of TiNiCo shape memory alloy.Scientific reports · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- Convergent Sensing: Integrating Biometric and Environmental Monitoring in Next-Generation Wearables.Biosensors · 2026Review
- Structuring integration for patient-centered care: a review-informed ontology-driven modular front-end framework for digital health innovation.Frontiers in digital health · 2026Article
- Advancing personalised and precision nutrition.Journal of nutritional science · 2026Review
- Dynamic prediction of the development of extrahepatic organ failures in ICU patients with decompensated cirrhosis.eGastroenterology · 2026Article
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
8 more citing papers are in PubMed but not listed here.
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
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
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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.