ArticleNPJ digital medicine2020
Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 6 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
124 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Improving Explainability and Integrability of Medical AI to Promote Health Care Professional Acceptance and Use: Mixed Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study.BMJ (Clinical research ed.) · 2023Pooled it
- Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.Applied clinical informatics · 2021Pooled it
- Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications.Journal of healthcare engineering · 2021Pooled it
- Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?World journal of nephrology · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- A temporal deep learning algorithm for prediction of extubation failures in critical care patients.Journal of clinical monitoring and computing · 2026Article
- Machine Learning-Based Prediction of Mononuclear Cell Collection Efficiency for CAR-T Manufacturing.Current medical science · 2026Article
- Mobile App Use and Pregnancy Health Literacy: Cross-Sectional Study Using Bayesian Network Analysis.Journal of medical Internet research · 2026Article
- How Best to Explain Machine Learning Models to Clinicians: A User Study of Explanation Types.medRxiv : the preprint server for health sciences · 2026Article
- Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.JMIR nursing · 2026Article
- The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach.Journal of intellectual disability research : JIDR · 2026Article
- The evolution of AI-integrated genome editing and its challenges.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Governing Generative AI for Healthy Ageing: A Normative Conceptual Framework for Societal Alignment, Epistemic Authority, and Value Convergence in Geriatric Care.Healthcare (Basel, Switzerland) · 2026Article
- Development and Validation of Machine Learning Models to Identify Emergency Department Patients at Increased Risk of New or Progressive Acute Kidney Injury.Journal of the American College of Emergency Physicians open · 2026Article
- A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.NPJ digital medicine · 2026Article
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- Vital signs as biomarkers of early clinical deterioration in pediatric emergency departments: physiology, interpretation, and innovations: a narrative review.International journal of emergency medicine · 2026Review
64 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
7 authors.
Funding
Abstract
Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.
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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.