Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration
Abstract
:1. Introduction
2. Literature Review
2.1. AI Applications in Healthcare
2.2. A Comprehensive Analysis of Innovation Resistance Models
2.3. Prior Studies on AI Resistance
3. Research Design
3.1. The Conduct of the Qualitative Study
3.1.1. Interview Guideline
3.1.2. Data Collection of the Qualitative Study
3.1.3. Participants
3.1.4. Data Analysis for the Qualitative Study
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- Homogeneity: units of analysis belong to the same register.
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- Mutual exclusion: a unit can only be assigned to one category.
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- Relevance: categories align with the content and theoretical framework.
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- Productivity: results must be information rich.
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- Objectivity: different coders should achieve the same results.
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- Simplification of answers without losing detail.
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- Identification of plausible themes, aspects, and typologies.
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- Identification of variables and their interrelations.
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- Development of tables highlighting results using simple statistical operations (e.g., percentage calculations).
3.1.5. Reliability and Validity of Qualitative Research
3.2. Hypothesis Development
3.2.1. The Need for Personal Contact
3.2.2. Perceived Technological Dependence
3.2.3. General Skepticism
3.3. The Conduct of the Quantitative Study
3.3.1. Data Collection of the Quantitative Study
3.3.2. Research Methods and Instrument
3.3.3. Data Analysis of the Quantitative Study
3.3.4. Demographics of Respondents Participating in the Quantitative Study
3.4. Ethical Considerations
4. Results
4.1. The Results of EFA
4.2. The Results of CFA
4.2.1. Goodness-of-Fit Indices for the Measurement Model
4.2.2. Convergent and Discriminant Validity
4.3. The Results of SEM
The Links Between Psychological Factors and Resistance to Use AI
5. Discussion
6. Implications
7. Limitation and Further Research Opportunities
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
List of Acronym
Acronym | Full Form |
AI | Artificial Intelligence |
IoMT | Internet of Medical Things |
SEM | Structural Equation Modeling |
AMOS | Analysis of Moment Structures |
EFA | Exploratory Factor Analysis |
CFA | Confirmatory Factor Analysis |
SPSS | Statistical Package for the Social Sciences |
NVIVO | Qualitative Data Analysis Software |
NPC | Need for Personal Contact |
PTD | Perceived Technological Dependence |
GSAI | General Skepticism toward AI |
AIH | AI in Healthcare |
SaMD | Software as a Medical Device |
TAM | Technology Acceptance Model |
UTAUT | Unified Theory of Acceptance and Use of Technology |
IRT | Innovation Resistance Theory |
ML | Machine Learning |
DL | Deep Learning |
NLP | Natural Language Processing |
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AI Techniques | Application in Healthcare |
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Medical Imaging Analysis (Oren et al., 2020; Manco et al., 2021; Pinto-Coelho, 2023). |
|
Predictive Analytics (Ghaffar Nia et al., 2023). |
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Charting, Chatbots, and Virtual Assistants (Agatstein, 2023; Alowais et al., 2023). |
|
AI-driven Robots (Deo & Anjankar, 2023; Denecke & Baudoin, 2022). |
|
Virtual Screening (Carracedo-Reboredo et al., 2021) |
|
Theory | Description | Authors |
---|---|---|
Status Quo Bias | The Status Quo Bias theory provides valuable insights into individuals’ inclination to maintain the status quo rather than embracing new systems, rooted in established psychological principles. It delineates psychological commitment, cognitive misperception, and rational decision-making as pivotal factors influencing decision inertia. While acknowledging the facilitating role of perceived value, the theory may oversimplify intricate decision processes and neglect external factors impacting decision-making dynamics. Despite offering a framework for comprehending resistance to change, it may not comprehensively encapsulate the nuanced dynamics of individual decision-making or accommodate situational influences. | Samuelson and Zeckhauser (1988), H. W. Kim and Kankanhalli (2009), K. Lee and Joshi (2017), Hajiheydari et al. (2021) |
Ram (1987) Theory | Proposes two elements influencing resistance to innovation: innovation characteristics and consumer characteristics. Innovation characteristics include the features and effects of new goods on consumers, while consumer characteristics are psychological traits influencing resistance. Ram and Sheth (1989) considered two categories of hurdles to innovation adoption: functional and psychological. The functional ones include subcategories such as usage, value, and risk hurdles, and are active forms of resistance stemming from the innovation’s characteristics and features (Heidenreich & Kraemer, 2016). These hurdles arise when adopting innovation necessitates significant changes, leading to concerns about risk, usage, and value. In contrast, psychological hurdles include traditional and image barriers, rooted in consumers’ existing worldviews and preexisting perceptions and traditions (Yu & Chantatub, 2016). | Ram (1987), Ram and Sheth (1989) |
Expanded Ram Model | Researchers expanded the Ram and Sheth model. Laukkanen and Kiviniemi (2010) explored the impact of company information on resistance barriers. Joachim et al. (2018) proposed a broader framework, including a more inclusive classification of product- and service-specific hurdles. Mani and Chouk (2018) introduced additional obstacles: technological vulnerability, and ideological and personal barriers. | Laukkanen and Kiviniemi (2010), Joachim et al. (2018), Mani and Chouk (2018) |
Yu and Lee Model | Refined Ram’s model of innovation resistance distinguishes between innovation resistance and hurdles. Yu and Lee proposed that only the customer and innovation aspects in Ram’s model give rise to innovation resistance, while the process of propagation acts as a societal barrier to innovation diffusion (C. Lee & Yu, 1994). | C. Lee and Yu (1994) |
Study | Focus | Key Findings |
---|---|---|
Alhashmi et al. (2019) | AI adoption barriers in Australian organizations | Identified barriers using the TOE framework; provided insights and a research agenda for executives and managers. |
Zhang et al. (2024) | AI in medical education | Review identified challenges including performance improvement, effectiveness, AI training data, and algorithms. |
Strohm et al. (2020) | Implementation barriers in clinical radiology | Inconsistent technical performance, unstructured processes, uncertain added value, and varying acceptance/trust. |
Cadario et al. (2021) | Resistance to medical AI | Challenges in understanding algorithms and illusory understanding of human decision-making; proposed interventions. |
Gao et al. (2020) | Social media analysis of attitudes toward AI doctors | Revealed positive attitudes tempered by concerns about technology maturity and company trustworthiness. |
Fujimori et al. (2022) | AI-based decision support systems in emergency departments | Highlighted system performance and compatibility as significant challenges. |
Ahmed et al. (2023) | Barriers to AI adoption in healthcare | A systematic review identified hurdles in six key areas: ethics, liability, regulatory, workforce, social, and patient safety; emphasized the need for understanding and overcoming these barriers for effective AI implementation in healthcare. |
Bhattacherjee and Hikmet (2007) | Theoretical model of physician resistance to HIT usage | Identified perceived threat and compatibility as key factors in resistance intentions. |
Gaczek et al. (2023) | Consumer resistance to AI healthcare recommendations | Impact of diagnosis trustworthiness and health anxiety; social proof as a mitigating factor. |
Mugabe (2021) | AI adoption in radiation oncology in New Zealand | Noted low levels of expertise as a hindrance to AI use. |
Jussupow et al. (2022) | Professional identity threats in medical AI resistance | Examined perceived self-threat, temporal distance of AI, and differences between students and professionals. |
Chaibi and Zaiem (2022) | Barriers to AI adoption among physicians in Tunisia | Poor infrastructure, including financial resources, specialized training, performance risks, perceived costs, technology dependency, and fears of AI replacing human jobs. |
Theme | Subtheme | Citation Times | % of the Theme | Verbatim | General Explanation |
---|---|---|---|---|---|
Psychological Factors Affecting Patient Resistance to AI | Need for Personal Contact | 38 | 88.37% |
| The sub-theme “Need for Personal Contact” highlights participants’ strong preference for human interaction, empathy, and trust in healthcare settings. Patients express concerns about AI replacing essential human qualities, such as emotional understanding, personalized care, and the human touch. The quotes emphasize that trust, emotional security, and face-to-face communication are vital for their well-being, which they believe AI cannot replicate. Participants also worry about AI’s inability to fully capture nuanced health details that doctors might identify in person. |
Perceived Technological dependence | 13 | 30.23% |
| The sub-theme “Perceived Technological Dependence” reflects participants’ concerns about over-reliance on AI in healthcare. The quotes highlight fears about losing human involvement in decision-making, the risk of technology failures, and the erosion of patients’ personal control over their care. Participants emphasize the importance of maintaining human intuition, judgment, and autonomy, which they feel are diminished when healthcare depends too heavily on AI. | |
General Skepticism | 17 | 39.53%. |
| The sub-theme “General Skepticism” captures participants’ doubts and lack of confidence in AI technology for healthcare. The quotes reflect concerns about the complexity and personal nature of healthcare, where participants prefer human judgment over machines. Fears of errors, misdiagnoses, and the unknown risks associated with AI highlight a general discomfort and distrust in relying on technology for critical health decisions. Participants also expressed reluctance to adopt AI due to feeling like test subjects for unproven technologies. |
Constructs | Measurement Items |
---|---|
The Need for Personal Contact (NPC) | NPC1. “I prefer to deal face-to-face with my doctor”. NPC2. “I am more reassured by dealing face-to-face with my doctor”. NPC3. “My particular service requirements are better served by doctors”. NPC4. “I prefer face-to-face contact to explain what I want to my doctor and to answer my questions”. NPC5. “I feel like I’m more in control when dealing with my doctor than with automated systems”. NPC6. “I like interacting with my doctor and medical staff in general”. |
Perceived Technological Dependence (PTD) | PTD1. “I am afraid of becoming dependent on AI technology”. PTD2. “I am afraid that my doctor become dependent on AI technology”. PTD3. “AI technology will reduce my autonomy and my doctor’s autonomy”. PTD4. “I think my social life will suffer from my use of AI technology”. |
General Skepticism Toward AI (GSAI) | GSAI1. “I am skeptical about AI technology”. GSAI2. “I do not think AI technology will be successful”. GSAI3. “I doubt that AI technology can actually do what its manufacturers promise”. |
Resistance to Use AI (RU) | RU1. “In sum, the possible use of AI technology to manage my health would cause problems that I don’t need”. RU2. “AI technology to manage my health would be connected with too many uncertainties”. RU3. “Using AI technology for managing my health is not for me”. RU4. “I am likely to be opposed to the use of AI technology for managing my health”. RU5. “I do not need AI technology to manage my health”. |
Factor Loadings | CR | AVE | p-Value | |
---|---|---|---|---|
The Need for Personal Contact | 0.989 | 0.935 | ||
NPC1 | 0.980 | *** | ||
NPC2 | 0.946 | *** | ||
NPC3 | 0.966 | *** | ||
NPC4 | 0.970 | *** | ||
NPC5 | 0.974 | *** | ||
NPC6 | 0.966 | |||
Perceived Technological Dependence | 0.974 | 0.902 | ||
PTD1 | 0.965 | *** | ||
PTD2 | 0.944 | *** | ||
PTD3 | 0.968 | *** | ||
PTD4 | 0.923 | |||
General Skepticism Towards AI | 0.976 | 0.931 | ||
GSAI1 | 0.959 | *** | ||
GSAI 2 | 0.968 | *** | ||
GSAI 3 | 0.967 | *** | ||
Resistance to Use AI | 0.981 | 0.911 | ||
RU1 | 0.945 | *** | ||
RU2 | 0.933 | *** | ||
RU3 | 0.966 | *** | ||
RU4 | 0.952 | *** | ||
RU5 | 0.975 | *** |
Hypotheses | Path Coefficient | Standard Error | C.R. | p Values | Results |
---|---|---|---|---|---|
NPC → RU | 0.515 | 0.021 | 17.058 | *** | Accepted |
PTD → RU | 0.620 | 0.023 | 18.973 | *** | Accepted |
GSAI → RU | 0.222 | 0.019 | 7.900 | *** | Accepted |
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© 2025 by the authors. Published by MDPI on behalf of the University Association of Education and Psychology. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Sobaih, A.E.E.; Chaibi, A.; Brini, R.; Abdelghani Ibrahim, T.M. Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration. Eur. J. Investig. Health Psychol. Educ. 2025, 15, 6. https://doi.org/10.3390/ejihpe15010006
Sobaih AEE, Chaibi A, Brini R, Abdelghani Ibrahim TM. Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration. European Journal of Investigation in Health, Psychology and Education. 2025; 15(1):6. https://doi.org/10.3390/ejihpe15010006
Chicago/Turabian StyleSobaih, Abu Elnasr E., Asma Chaibi, Riadh Brini, and Tamer Mohamed Abdelghani Ibrahim. 2025. "Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration" European Journal of Investigation in Health, Psychology and Education 15, no. 1: 6. https://doi.org/10.3390/ejihpe15010006
APA StyleSobaih, A. E. E., Chaibi, A., Brini, R., & Abdelghani Ibrahim, T. M. (2025). Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration. European Journal of Investigation in Health, Psychology and Education, 15(1), 6. https://doi.org/10.3390/ejihpe15010006