Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration
Artificial intelligence (AI) has transformed healthcare, yet patients’ acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack of comprehensive research exists that examines the variables dri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | European Journal of Investigation in Health, Psychology and Education |
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| Online Access: | https://www.mdpi.com/2254-9625/15/1/6 |
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| author | Abu Elnasr E. Sobaih Asma Chaibi Riadh Brini Tamer Mohamed Abdelghani Ibrahim |
| author_facet | Abu Elnasr E. Sobaih Asma Chaibi Riadh Brini Tamer Mohamed Abdelghani Ibrahim |
| author_sort | Abu Elnasr E. Sobaih |
| collection | DOAJ |
| description | Artificial intelligence (AI) has transformed healthcare, yet patients’ acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack of comprehensive research exists that examines the variables driving patients’ resistance to AI. This study explores the variables influencing patients’ resistance to adopt AI technology in healthcare by applying an extended Ram and Sheth Model. More specifically, this research examines the roles of the need for personal contact (NPC), perceived technological dependence (PTD), and general skepticism toward AI (GSAI) in shaping patient resistance to AI integration. For this reason, a sequential mixed-method approach was employed, beginning with semi-structured interviews to identify adaptable factors in healthcare. It then followed with a survey to validate the qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The findings confirm that NPC, PTD, and GSAI significantly contribute to patient resistance to AI in healthcare. Precisely, patients who prefer personal interaction, feel dependent on AI, or are skeptical of AI’s promises are more likely to resist its adoption. The findings highlight the psychological factors driving patient reluctance toward AI in healthcare, offering valuable insights for healthcare administrators. Strategies to balance AI’s efficiency with human interaction, mitigate technological dependence, and foster trust are recommended for successful implementation of AI. This research adds to the theoretical understanding of Innovation Resistance Theory, providing both conceptual insights and practical implications for the effective incorporation of AI in healthcare. |
| format | Article |
| id | doaj-art-c95570e012184053851c34dbb81d5b50 |
| institution | Kabale University |
| issn | 2174-8144 2254-9625 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | European Journal of Investigation in Health, Psychology and Education |
| spelling | doaj-art-c95570e012184053851c34dbb81d5b502025-01-24T13:30:40ZengMDPI AGEuropean Journal of Investigation in Health, Psychology and Education2174-81442254-96252025-01-01151610.3390/ejihpe15010006Unlocking Patient Resistance to AI in Healthcare: A Psychological ExplorationAbu Elnasr E. Sobaih0Asma Chaibi1Riadh Brini2Tamer Mohamed Abdelghani Ibrahim3Management Department, College of Business Administration, King Faisal University, Al-Ahsaa 31982, Saudi ArabiaManagement Department, Mediterranean School of Business (MSB), South Mediterranean University, Tunis 1053, TunisiaDepartment of Business Administration, College of Business Administration, Majmaah University, Al Majma’ah 11952, Saudi ArabiaSocial Studies Department, Faculty of Arts, King Faisal University, Al-Ahsa 31982, Saudi ArabiaArtificial intelligence (AI) has transformed healthcare, yet patients’ acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack of comprehensive research exists that examines the variables driving patients’ resistance to AI. This study explores the variables influencing patients’ resistance to adopt AI technology in healthcare by applying an extended Ram and Sheth Model. More specifically, this research examines the roles of the need for personal contact (NPC), perceived technological dependence (PTD), and general skepticism toward AI (GSAI) in shaping patient resistance to AI integration. For this reason, a sequential mixed-method approach was employed, beginning with semi-structured interviews to identify adaptable factors in healthcare. It then followed with a survey to validate the qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The findings confirm that NPC, PTD, and GSAI significantly contribute to patient resistance to AI in healthcare. Precisely, patients who prefer personal interaction, feel dependent on AI, or are skeptical of AI’s promises are more likely to resist its adoption. The findings highlight the psychological factors driving patient reluctance toward AI in healthcare, offering valuable insights for healthcare administrators. Strategies to balance AI’s efficiency with human interaction, mitigate technological dependence, and foster trust are recommended for successful implementation of AI. This research adds to the theoretical understanding of Innovation Resistance Theory, providing both conceptual insights and practical implications for the effective incorporation of AI in healthcare.https://www.mdpi.com/2254-9625/15/1/6artificial intelligence (AI)AI in healthcarepatient resistanceperceived technological dependencepsychological variablesskepticism toward AI |
| spellingShingle | Abu Elnasr E. Sobaih Asma Chaibi Riadh Brini Tamer Mohamed Abdelghani Ibrahim Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration European Journal of Investigation in Health, Psychology and Education artificial intelligence (AI) AI in healthcare patient resistance perceived technological dependence psychological variables skepticism toward AI |
| title | Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration |
| title_full | Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration |
| title_fullStr | Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration |
| title_full_unstemmed | Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration |
| title_short | Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration |
| title_sort | unlocking patient resistance to ai in healthcare a psychological exploration |
| topic | artificial intelligence (AI) AI in healthcare patient resistance perceived technological dependence psychological variables skepticism toward AI |
| url | https://www.mdpi.com/2254-9625/15/1/6 |
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