Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908818908119040 |
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| author | Ruffle, James K Mohinta, Samia Pombo, Guilherme Biswas, Asthik Campbell, Alan Davagnanam, Indran Doig, David Hammam, Ahmed Hyare, Harpreet Jabeen, Farrah Lim, Emma Mallon, Dermot Owen, Stephanie Wilkinson, Sophie Brandner, Sebastian Nachev, Parashkev |
| author_facet | Ruffle, James K Mohinta, Samia Pombo, Guilherme Biswas, Asthik Campbell, Alan Davagnanam, Indran Doig, David Hammam, Ahmed Hyare, Harpreet Jabeen, Farrah Lim, Emma Mallon, Dermot Owen, Stephanie Wilkinson, Sophie Brandner, Sebastian Nachev, Parashkev |
| contents | The benefits of artificial intelligence (AI) human partnerships-evaluating how AI agents enhance expert human performance-are increasingly studied. Though rarely evaluated in healthcare, an inverse approach is possible: AI benefiting from the support of an expert human agent. Here, we investigate both human-AI clinical partnership paradigms in the magnetic resonance imaging-guided characterisation of patients with brain tumours. We reveal that human-AI partnerships improve accuracy and metacognitive ability not only for radiologists supported by AI, but also for AI agents supported by radiologists. Moreover, the greatest patient benefit was evident with an AI agent supported by a human one. Synergistic improvements in agent accuracy, metacognitive performance, and inter-rater agreement suggest that AI can create more capable, confident, and consistent clinical agents, whether human or model-based. Our work suggests that the maximal value of AI in healthcare could emerge not from replacing human intelligence, but from AI agents that routinely leverage and amplify it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance Ruffle, James K Mohinta, Samia Pombo, Guilherme Biswas, Asthik Campbell, Alan Davagnanam, Indran Doig, David Hammam, Ahmed Hyare, Harpreet Jabeen, Farrah Lim, Emma Mallon, Dermot Owen, Stephanie Wilkinson, Sophie Brandner, Sebastian Nachev, Parashkev Human-Computer Interaction Artificial Intelligence Machine Learning Multiagent Systems The benefits of artificial intelligence (AI) human partnerships-evaluating how AI agents enhance expert human performance-are increasingly studied. Though rarely evaluated in healthcare, an inverse approach is possible: AI benefiting from the support of an expert human agent. Here, we investigate both human-AI clinical partnership paradigms in the magnetic resonance imaging-guided characterisation of patients with brain tumours. We reveal that human-AI partnerships improve accuracy and metacognitive ability not only for radiologists supported by AI, but also for AI agents supported by radiologists. Moreover, the greatest patient benefit was evident with an AI agent supported by a human one. Synergistic improvements in agent accuracy, metacognitive performance, and inter-rater agreement suggest that AI can create more capable, confident, and consistent clinical agents, whether human or model-based. Our work suggests that the maximal value of AI in healthcare could emerge not from replacing human intelligence, but from AI agents that routinely leverage and amplify it. |
| title | Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2512.19707 |