Trust perceptions towards XAI in healthcare
This study explores the perceptions of medical professionals towards artificial intelligence (AI) and the influence of Explainable AI (XAI) algorithms on their trust in AI. The research focuses on one-on-one interviews conducted with 12 medical students, divided into two groups of six, each group ex...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175388 |
| _version_ | 1826125935245524992 |
|---|---|
| author | Cai, Xinrui |
| author2 | Fan Xiuyi |
| author_facet | Fan Xiuyi Cai, Xinrui |
| author_sort | Cai, Xinrui |
| collection | NTU |
| description | This study explores the perceptions of medical professionals towards artificial intelligence (AI) and the influence of Explainable AI (XAI) algorithms on their trust in AI. The research focuses on one-on-one interviews conducted with 12 medical students, divided into two groups of six, each group exposed to one of two XAI algorithms: SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), explaining how Random Forest Classifier predicted lung cancer from a set of predictive attributes including demographic information, basic health metrics, lifestyle habits and findings from genetic PCA (Principal Component Analyses). Additionally, online surveys were administered to 50 medical students on the same case as interviews. Both interviews and surveys had selected participants ensuring equal representation from two medical schools in Singapore, with considerations for gender and self-rated confidence in AI knowledge.
Qualitative data from interviews were analysed using Reflexive Thematic Analysis, revealing themes related to trust in AI and perceptions of XAI algorithms. Quantitative data were analysed using Microsoft Excel to visualize trends and patterns in the survey responses. Four research questions were developed in this study and the findings suggest that the type of XAI algorithm used does not significant impact medical professionals' trust in AI, and XAI’s impact on trust of medical professionals on AI may not be direct and requires more research and improving on XAI output to achieve its intended purposes. This study contributes to the understanding of how XAI can enhance trust in AI among medical professionals, with implications for the design and implementation of AI systems in healthcare settings. |
| first_indexed | 2024-10-01T06:44:51Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175388 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:44:51Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1753882024-04-26T15:43:02Z Trust perceptions towards XAI in healthcare Cai, Xinrui Fan Xiuyi School of Computer Science and Engineering xyfan@ntu.edu.sg Computer and Information Science This study explores the perceptions of medical professionals towards artificial intelligence (AI) and the influence of Explainable AI (XAI) algorithms on their trust in AI. The research focuses on one-on-one interviews conducted with 12 medical students, divided into two groups of six, each group exposed to one of two XAI algorithms: SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), explaining how Random Forest Classifier predicted lung cancer from a set of predictive attributes including demographic information, basic health metrics, lifestyle habits and findings from genetic PCA (Principal Component Analyses). Additionally, online surveys were administered to 50 medical students on the same case as interviews. Both interviews and surveys had selected participants ensuring equal representation from two medical schools in Singapore, with considerations for gender and self-rated confidence in AI knowledge. Qualitative data from interviews were analysed using Reflexive Thematic Analysis, revealing themes related to trust in AI and perceptions of XAI algorithms. Quantitative data were analysed using Microsoft Excel to visualize trends and patterns in the survey responses. Four research questions were developed in this study and the findings suggest that the type of XAI algorithm used does not significant impact medical professionals' trust in AI, and XAI’s impact on trust of medical professionals on AI may not be direct and requires more research and improving on XAI output to achieve its intended purposes. This study contributes to the understanding of how XAI can enhance trust in AI among medical professionals, with implications for the design and implementation of AI systems in healthcare settings. Bachelor's degree 2024-04-24T01:01:31Z 2024-04-24T01:01:31Z 2024 Final Year Project (FYP) Cai, X. (2024). Trust perceptions towards XAI in healthcare. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175388 https://hdl.handle.net/10356/175388 en SCSE23-0700 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Cai, Xinrui Trust perceptions towards XAI in healthcare |
| title | Trust perceptions towards XAI in healthcare |
| title_full | Trust perceptions towards XAI in healthcare |
| title_fullStr | Trust perceptions towards XAI in healthcare |
| title_full_unstemmed | Trust perceptions towards XAI in healthcare |
| title_short | Trust perceptions towards XAI in healthcare |
| title_sort | trust perceptions towards xai in healthcare |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/175388 |
| work_keys_str_mv | AT caixinrui trustperceptionstowardsxaiinhealthcare |