ReviewNPJ digital medicine2019
Putting the data before the algorithm in big data addressing personalized healthcare.
Review in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 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
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Digital Twins for Targeted Therapy in Head and Neck Cancer: From Molecular Stratification to Resistance-Aware Combination Strategies.Current oncology (Toronto, Ont.) · 2026Review
- Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026Review
- Deep Learning outperforms physicians in myopathy and neuropathy classification based on Needle Electromyography Signal.PloS one · 2026Article
- A Multilateral Qualitative Study of Perspectives on Enhancing Clinical Trial Diversity Among Historically Underrepresented Groups.Clinical therapeutics · 2025Article
- Evaluating the impact of data biases on algorithmic fairness and clinical utility of machine learning models for prolonged opioid use prediction.JAMIA open · 2025Article
- The ethics of data mining in healthcare: challenges, frameworks, and future directions.BioData mining · 2025Review
- AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning.World journal of psychiatry · 2025Article
- Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders.Journal of occupational rehabilitation · 2025Article
- Artificial intelligence-assisted organoid construction in congenital heart disease: current applications and future prospects.Frontiers in bioengineering and biotechnology · 2025Review
- Does Personalized Mean Popular? A Content Analysis of Suicide Prevention Smartphone Applications.Current psychology (New Brunswick, N.J.) · 2024Article
- Ancestry-associated co-alteration landscape of KRAS and EGFR-altered non-squamous NSCLC.NPJ precision oncology · 2024Article
- Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism.BMC medical informatics and decision making · 2024Article
- Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.JCO clinical cancer informatics · 2024Article
- Barriers to Recruitment and Retention Among Underrepresented Populations in Cancer Clinical Trials: A Qualitative Study of the Perspectives of Clinical Trial Research Coordinating Staff at a Cancer Center.Journal of healthcare leadership · 2024Article
- AI-enabled organoids: Construction, analysis, and application.Bioactive materials · 2024Review
- Insufficient Reporting of Race and Ethnicity in Breast Cancer Clinical Trials.Annals of surgical oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technologies leveraging big data, including predictive algorithms and machine learning, are playing an increasingly important role in the delivery of healthcare. However, evidence indicates that such algorithms have the potential to worsen disparities currently intrinsic to the contemporary healthcare system, including racial biases. Blame for these deficiencies has often been placed on the algorithm-but the underlying training data bears greater responsibility for these errors, as biased outputs are inexorably produced by biased inputs. The utility, equity, and generalizability of predictive models depend on population-representative training data with robust feature sets. So while the conventional paradigm of big data is deductive in nature-clinical decision support-a future model harnesses the potential of big data for inductive reasoning. This may be conceptualized as clinical decision questioning, intended to liberate the human predictive process from preconceived lenses in data solicitation and/or interpretation. Efficacy, representativeness and generalizability are all heightened in this schema. Thus, the possible risks of biased big data arising from the inputs themselves must be acknowledged and addressed. Awareness of data deficiencies, structures for data inclusiveness, strategies for data sanitation, and mechanisms for data correction can help realize the potential of big data for a personalized medicine era. Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data.
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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.