Patient-Centred Explainability in IVF Outcome Prediction
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912444996124672 |
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| author | Sivaprasad, Adarsa Reiter, Ehud McLernon, David Tintarev, Nava Bhattacharya, Siladitya Oren, Nir |
| author_facet | Sivaprasad, Adarsa Reiter, Ehud McLernon, David Tintarev, Nava Bhattacharya, Siladitya Oren, Nir |
| contents | This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse four years of anonymous patient feedback, followed by a user survey and interviews to quantify trust and understandability. Results highlight a lay user's need for prediction model \emph{explainability} beyond the model feature space. We identify user concerns about data shifts and model exclusions that impact trust. The results call attention to the shortcomings of current practices in explainable AI research and design and the need for explainability beyond model feature space and epistemic assumptions, particularly in high-stakes healthcare contexts where users gather extensive information and develop complex mental models. To address these challenges, we propose a dialogue-based interface and explore user expectations for personalised explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18760 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Patient-Centred Explainability in IVF Outcome Prediction Sivaprasad, Adarsa Reiter, Ehud McLernon, David Tintarev, Nava Bhattacharya, Siladitya Oren, Nir Human-Computer Interaction This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse four years of anonymous patient feedback, followed by a user survey and interviews to quantify trust and understandability. Results highlight a lay user's need for prediction model \emph{explainability} beyond the model feature space. We identify user concerns about data shifts and model exclusions that impact trust. The results call attention to the shortcomings of current practices in explainable AI research and design and the need for explainability beyond model feature space and epistemic assumptions, particularly in high-stakes healthcare contexts where users gather extensive information and develop complex mental models. To address these challenges, we propose a dialogue-based interface and explore user expectations for personalised explanations. |
| title | Patient-Centred Explainability in IVF Outcome Prediction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.18760 |