The case for delegated AI autonomy for Human AI teaming in healthcare
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913754398064640 |
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| author | Jia, Yan Evans, Harriet Porter, Zoe Graham, Simon McDermid, John Lawton, Tom Snead, David Habli, Ibrahim |
| author_facet | Jia, Yan Evans, Harriet Porter, Zoe Graham, Simon McDermid, John Lawton, Tom Snead, David Habli, Ibrahim |
| contents | In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act autonomously for a subset of patient cases whilst serving a supportive role in other subsets of patient cases based on defined delegation criteria. By leveraging the complementary strengths of both humans and AI, it aims to deliver greater overall performance than existing human-AI teaming models. It ensures safe handling of patient cases and potentially reduces clinician review time, whilst being mindful of AI tool limitations. After setting the approach within the context of current human-AI teaming models, we outline the delegation criteria and apply them to a specific AI-based tool used in histopathology. The potential impact of the approach and the regulatory requirements for its successful implementation are then discussed. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_18778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The case for delegated AI autonomy for Human AI teaming in healthcare Jia, Yan Evans, Harriet Porter, Zoe Graham, Simon McDermid, John Lawton, Tom Snead, David Habli, Ibrahim Artificial Intelligence Human-Computer Interaction Machine Learning In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act autonomously for a subset of patient cases whilst serving a supportive role in other subsets of patient cases based on defined delegation criteria. By leveraging the complementary strengths of both humans and AI, it aims to deliver greater overall performance than existing human-AI teaming models. It ensures safe handling of patient cases and potentially reduces clinician review time, whilst being mindful of AI tool limitations. After setting the approach within the context of current human-AI teaming models, we outline the delegation criteria and apply them to a specific AI-based tool used in histopathology. The potential impact of the approach and the regulatory requirements for its successful implementation are then discussed. |
| title | The case for delegated AI autonomy for Human AI teaming in healthcare |
| topic | Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2503.18778 |