ArticleInteractive journal of medical research2019
Artificial Intelligence in Clinical Health Care Applications: Viewpoint.
Article in Interactive journal of medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.PloS one · 2024Pooled it
- When evidence meets artificial intelligence.Lancet regional health. Americas · 2026Review
- Use of Artificial Intelligence in Adolescents' Mental Health Care: Systematic Scoping Review of Current Applications and Future Directions.JMIR mental health · 2025Article
- Applications of Self-Driving Vehicles in an Aging Population.JMIR formative research · 2025Article
- Open challenges and opportunities in federated foundation models towards biomedical healthcare.BioData mining · 2025Review
- Machine learning-based prediction of mortality in pediatric trauma patients.Frontiers in pediatrics · 2025Article
- The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.Human vaccines & immunotherapeutics · 2024Article
- Article
- Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops.Journal of neuroengineering and rehabilitation · 2024Article
- The use of artificial intelligence in the treatment of rare diseases: A scoping review.Intractable & rare diseases research · 2024Article
- Knowledge, Attitude and Practice Toward Artificial Intelligence Among Healthcare Workers in Private Polyclinics in Jeddah, Saudi Arabia.Advances in medical education and practice · 2024Article
- Evaluation of the Artificial Intelligence Chatbot on Breast Reconstruction and Its Efficacy in Surgical Research: A Case Study.Aesthetic plastic surgery · 2023Article
- A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases.International journal of molecular sciences · 2023Review
- Aesthetic Surgery Advice and Counseling from Artificial Intelligence: A Rhinoplasty Consultation with ChatGPT.Aesthetic plastic surgery · 2023Observational
- Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector.Biomedical materials & devices (New York, N.Y.) · 2023Review
- Assessing the Level of Understanding (Knowledge) and Awareness of Diagnostic Imaging Students in Ghana on Artificial Intelligence and Its Applications in Medical Imaging.Radiology research and practice · 2023Article
- Human-AI teaming: leveraging transactive memory and speaking up for enhanced team effectiveness.Frontiers in psychology · 2023Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study.Journal of medical Internet research · 2021Article
- Role of deep learning in early detection of COVID-19: Scoping review.Computer methods and programs in biomedicine update · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The idea of artificial intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently, we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning. Healthcare is considered the next domain to be revolutionized by artificial intelligence. While AI approaches are excellently suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose six recommendations-the 6Rs-to improve AI projects in the biomedical space, especially clinical health care, and to facilitate communication between AI scientists and medical doctors: (1) Relevant and well-defined clinical question first; (2) Right data (ie, representative and of good quality); (3) Ratio between number of patients and their variables should fit the AI method; (4) Relationship between data and ground truth should be as direct and causal as possible; (5) Regulatory ready; enabling validation; and (6) Right AI method.
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.