Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917145043009536 |
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| author | Xu, Xuhai Hu, Haoyu Zhang, Haoran Wang, Will Ke Wang, Reina Soenksen, Luis R. Badri, Omar Jafry, Sheharbano Burger, Elise Nwandu, Lotanna Mehta, Apoorva Duhaime, Erik P. Qasim, Asif Lin, Hause Pereira, Janis Hershon, Jonathan Mui, Paulius Gru, Alejandro A. Elhadad, Noémie Mamykina, Lena Groh, Matthew Tschandl, Philipp Daneshjou, Roxana Ghassemi, Marzyeh |
| author_facet | Xu, Xuhai Hu, Haoyu Zhang, Haoran Wang, Will Ke Wang, Reina Soenksen, Luis R. Badri, Omar Jafry, Sheharbano Burger, Elise Nwandu, Lotanna Mehta, Apoorva Duhaime, Erik P. Qasim, Asif Lin, Hause Pereira, Janis Hershon, Jonathan Mui, Paulius Gru, Alejandro A. Elhadad, Noémie Mamykina, Lena Groh, Matthew Tschandl, Philipp Daneshjou, Roxana Ghassemi, Marzyeh |
| contents | Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12500 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public Xu, Xuhai Hu, Haoyu Zhang, Haoran Wang, Will Ke Wang, Reina Soenksen, Luis R. Badri, Omar Jafry, Sheharbano Burger, Elise Nwandu, Lotanna Mehta, Apoorva Duhaime, Erik P. Qasim, Asif Lin, Hause Pereira, Janis Hershon, Jonathan Mui, Paulius Gru, Alejandro A. Elhadad, Noémie Mamykina, Lena Groh, Matthew Tschandl, Philipp Daneshjou, Roxana Ghassemi, Marzyeh Human-Computer Interaction Artificial Intelligence Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design. |
| title | Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2512.12500 |