ReviewYearbook of medical informatics2019
An Open Science Approach to Artificial Intelligence in Healthcare.
Review in Yearbook of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 47 citations in OpenAlex.
- Artificial Intelligence Competencies and Educational Needs Among ERNICA Members: Results of a Multinational Survey.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Article
- Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies.Journal of medical Internet research · 2026Article
- "I don't see anything specifically about Black/African Americans." Testing an Alzheimer-specific generative AI tool tailored for African American/Black communities.ACM transactions on computing for healthcare · 2026Article
- Integrating Artificial Intelligence into Biomedical Science Curricula: Advancing Healthcare Education.Clinics and practice · 2024Review
- Intelligent oncology: The convergence of artificial intelligence and oncology.Journal of the National Cancer Center · 2023Review
- The role of digital social innovations to address SDGs: A systematic review.Environment, development and sustainability · 2023Review
- Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information.Molecular pharmaceutics · 2023Article
- FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.Journal of biomedical informatics · 2022Article
- Use of automated artificial intelligence to predict the need for orthodontic extractions.Korean journal of orthodontics · 2022Article
- Cardiovascular informatics: building a bridge to data harmony.Cardiovascular research · 2022Review
- Digital Health Policy and Programs for Hospital Care in Vietnam: Scoping Review.Journal of medical Internet research · 2022Article
- Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study.Journal of medical Internet research · 2021Article
- A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.BMC medical informatics and decision making · 2021Article
- Using Open Source, Open Data, and Civic Technology to Address the COVID-19 Pandemic and Infodemic.Yearbook of medical informatics · 2021Article
- Evidence-Based Health Informatics as the Foundation for the COVID-19 Response: A Joint Call for Action.Methods of information in medicine · 2020Article
- Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes.Frontiers in digital health · 2020Article
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 at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesArtificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an "open science" approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems.
methodIn response to the call for participation for the 2019 International Medical Informatics Association (IMIA) Yearbook theme issue on AI in healthcare, the IMIA Open Source Working Group conducted a rapid review of recent literature relating to open science and AI in healthcare and discussed how an open science approach could help overcome concerns about the adoption of new AI technology in healthcare settings.
resultsThe recent literature reveals that open science approaches to AI system development are well established. The ecosystem of software development, data sharing, education, and research in the AI community has, in general, adopted an open science ethos that has driven much of the recent innovation and adoption of new AI techniques. However, within the healthcare domain, adoption may be inhibited by the use of "black-box" AI systems, where only the inputs and outputs of those systems are understood, and clinical effectiveness and implementation studies are missing.
conclusionsAs AI-based data analysis and clinical decision support systems begin to be implemented in healthcare systems around the world, further openness of clinical effectiveness and mechanisms of action may be required by safety-conscious healthcare policy-makers to ensure they are clinically effective in real world use.
Indexed as
Identifiers
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.