ReviewInternational journal of environmental research and public health2021
A Review on Human-AI Interaction in Machine Learning and Insights for Medical Applications.
Review in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 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
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.Journal of medical Internet research · 2025Pooled it
- When combinations of humans and AI are useful: A systematic review and meta-analysis.Nature human behaviour · 2024Pooled it
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Article
- Generative AI in simulation debriefings: an exploratory study using the Team-FIRST framework and qualitative feedback from simulation experts and learners.Advances in simulation (London, England) · 2026Article
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
- Effectiveness and reliability of AI in diagnosis and robot-assisted spinal and cranial surgery: efficient outcomes and ethical worries.Annals of medicine and surgery (2012) · 2025Review
- The triglyceride-glucose index positively associates with the prevalence and severity of coronary heart disease in patients among hypertension.Scientific reports · 2025Article
- Toward a human-centric co-design methodology for AI detection of differences between planned and delivered dose in radiotherapy.Journal of applied clinical medical physics · 2025Article
- Assessing the Accuracy, Completeness and Safety of ChatGPT-4o Responses on Pressure Injuries in Infants: Clinical Applications and Future Implications.Nursing reports (Pavia, Italy) · 2025Article
- Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.BMC medical research methodology · 2025Article
- AN INTELLIGIBLE AI-DRIVEN DECISION SUPPORT SYSTEM FOR POSTSTROKE MOBILITY ASSESSMENT.Journal of rehabilitation medicine. Clinical communications · 2025Article
- Research Trends and Dynamics in Single-cell RNA Sequencing for Musculoskeletal Diseases: A Scientometric and Visualization Study.International journal of medical sciences · 2025Review
- Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models.Diagnostics (Basel, Switzerland) · 2024Article
- The most efficient machine learning algorithms in stroke prediction: A systematic review.Health science reports · 2024Review
- Reply to: False conflict and false confirmation errors are crucial components of AI accuracy in medical decision making.Nature communications · 2024Article
- Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model.Briefings in bioinformatics · 2024Article
- Derivation and Experimental Performance of Standard and Novel Uncertainty Calibration Techniques.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Development and use of machine learning models for prediction of male sling success A proof-of-concept institutional evaluation.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo provide a human-Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches.
methodsA scoping literature review is performed on Scopus and Google Scholar using the terms "human in the loop", "human in the loop machine learning", and "interactive machine learning". Peer-reviewed papers published from 2015 to 2020 are included in our review.
resultsWe design four questions to investigate and describe human-AI interaction in ML applications. These questions are "Why should humans be in the loop?", "Where does human-AI interaction occur in the ML processes?", "Who are the humans in the loop?", and "How do humans interact with ML in Human-In-the-Loop ML (HILML)?". To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human-AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human-AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.
Indexed as
Identifiers
What Socratic 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 Socratic graph.