ArticleProceedings of the National Academy of Sciences of the United States of America2022
Bayesian modeling of human-AI complementarity.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human learning is an understudied but promising lever for boosting human-AI synergy.Proceedings of the National Academy of Sciences of the United States of America · 2026Pooled it
- Benchmarking large language models against practicing clinicians on psychopathological assessment.NPJ digital medicine · 2026Article
- AI-Driven Insights into Glabellar Wrinkle Patterns: Reassessing the Standardised Botulinum Toxin: An Injection Protocol to Address Anatomical Variability.Aesthetic plastic surgery · 2026Article
- Adaptive yet suboptimal integration of advice in decision-making.Communications psychology · 2026Article
- Psychophysiological markers of trust in automation: insights from ERP responses in a modified flanker task.Cognitive research: principles and implications · 2026Article
- Drivers and influence of social conformity on decision making in human-AI teams.Scientific reports · 2026Article
- Toward a science of human-AI teaming for decision making: A complementarity framework.PNAS nexus · 2026Article
- Confidence-weighted integration of human and machine judgments for superior decision-making.Patterns (New York, N.Y.) · 2026Article
- On Benchmarking Human-like Intelligence in Machines.Open mind : discoveries in cognitive science · 2026Article
- Automated detection of freeze-thaw signatures in archaeological sediments using deep learning.Open research Europe · 2026Article
- VENturing into machine learning for the morphological analysis of von Economo neurons.Scientific reports · 2025Article
- Deliberation in Guesstimation.Cognitive science · 2025Article
- Human-AI collectives most accurately diagnose clinical vignettes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integrating Psychological Insights into Aesthetic Medicine: A Cross-Generational Analysis of Patient Archetypes (IMPACT Study).Aesthetic plastic surgery · 2025Article
- Large language models surpass human experts in predicting neuroscience results.Nature human behaviour · 2025Article
- Equation for Attractiveness: Integrating Multidimensional Factors Through Computational Neuroaesthetics.Aesthetic plastic surgery · 2025Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Building machines that learn and think with people.Nature human behaviour · 2024Review
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- Three Challenges for AI-Assisted Decision-Making.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SignificanceWith the increase in artificial intelligence in real-world applications, there is interest in building hybrid systems that take both human and machine predictions into account. Previous work has shown the benefits of separately combining the predictions of diverse machine classifiers or groups of people. Using a Bayesian modeling framework, we extend these results by systematically investigating the factors that influence the performance of hybrid combinations of human and machine classifiers while taking into account the unique ways human and algorithmic confidence is expressed.
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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.