ArticleSchizophrenia bulletin2022
Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies.
Article in Schizophrenia bulletin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
- Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications.Entropy (Basel, Switzerland) · 2026Review
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- Article
- AI assisted triage of UK patients in mental health care services: a qualitative focus group study of patients' attitudes.BMC psychiatry · 2026Article
- Quantitative semiology: harnessing AI-generated teaching signals in psychiatry.Journal of psychiatry & neuroscience : JPN · 2025Article
- Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology.Nature cancer · 2025Article
- Quantitative Research on Digitalized Treatment Options for Older Adults With Mental Illness: Scoping Review.JMIR mental health · 2025Article
- Representation of Demographics in Otolaryngology by Artificial Intelligence Text-to-Image Platforms.Laryngoscope investigative otolaryngology · 2025Article
- Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures.Journal of clinical medicine · 2025Review
- The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review.Journal of medical Internet research · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- Deep learning in obsessive-compulsive disorder: a narrative review.Frontiers in psychiatry · 2025Review
- HTAD: a human-in-the-loop framework for supervised chromatin domain detection.Genome biology · 2024Article
- Unveiling readability challenges: An extensive analysis of consent document accessibility in clinical trials.Journal of clinical and translational science · 2024Article
- Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop.Schizophrenia bulletin · 2023Article
- AI and professional liability assessment in healthcare. A revolution in legal medicine?Frontiers in medicine · 2023Review
- Language abnormalities in schizophrenia: binding core symptoms through contemporary empirical evidence.Schizophrenia (Heidelberg, Germany) · 2022Review
- Translating Natural Language Processing into Mainstream Schizophrenia Assessment.Schizophrenia bulletin · 2022Article
- What's That Noise? Interpreting Algorithmic Interpretation of Human Speech as a Legal and Ethical Challenge.Schizophrenia bulletin · 2022Article
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
objectivesMachine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach as operating in isolation without much need for human involvement, yet it remains crucial to harness human-in-the-loop practices when developing and implementing such techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and reliability of the process.
methodsWe showcase pitfalls of the traditional ML framework and explain how it can be improved with human-in-the-loop techniques. Specifically, we applied active learning strategies to the automatic scoring of a story recall task and compared the results to a traditional approach.
resultsHuman-in-the-loop methodologies supplied a greater understanding of where the model was least confident or had knowledge gaps during training. As compared to the traditional framework, less than half of the training data were needed to reach a given accuracy.
conclusionsHuman-in-the-loop ML is an approach to data collection and model creation that harnesses active learning to select the most critical data needed to increase a model's accuracy and generalizability more efficiently than classic random sampling would otherwise allow. Such techniques may additionally operate as safeguards from spurious predictions and can aid in decreasing disparities that artificial intelligence systems otherwise propagate.
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.