ArticleBMC medicine2018
From hype to reality: data science enabling personalized medicine.
Article in BMC medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers, 4 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
170 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A systematic review and meta-analysis of disease clusters in multimorbidity.Nature communications · 2025Pooled it
- Pseudonymization tools for medical research: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Grilling the data: application of specification curve analysis to red meat and all-cause mortality.Journal of clinical epidemiology · 2024Pooled it
- Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.BMC medical informatics and decision making · 2021Pooled it
- What Is Precision Psychotherapy?The Journal of medicine and philosophy · 2026Article
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Nanostructured lipid carriers as co-delivery systems for cancer therapy: Prospects and challenges.International journal of pharmaceutics: X · 2026Review
- Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality.Brain sciences · 2026Review
- Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.Intensive care medicine experimental · 2026Article
- Online tutorial on survival analysis for biomarker discovery.PLoS computational biology · 2026Article
- Machine learning for predicting CKD stages in patients with autosomal dominant polycystic kidney disease: a nationwide cohort study in Japan.Scientific reports · 2026Article
- SPHN Connector - a scalable pipeline for generating validated knowledge graphs from federated and semantically enriched health data.BMC medical informatics and decision making · 2026Article
- Uncovering distinct clinical phenotypes in disseminated intravascular coagulation through machine learning-enabled cluster analysis.Frontiers in molecular biosciences · 2026Article
- PLAID: ultrafast single-sample gene set enrichment scoring.Bioinformatics (Oxford, England) · 2025Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- Improving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort.Research square · 2025Article
- Article
- MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists.Scientific reports · 2025Article
- Explainable deep learning for stratified medicine in inflammatory bowel disease.Genome biology · 2025Article
110 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
20 authors.
Funding
Abstract
backgroundPersonalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future.
conclusionsThere is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
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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.