This detailed introductory text is based on the article “Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration” by Sobaih, A. E. E., Chaibi, A., Brini, R., & Abdelghani Ibrahim, T. M., published in the European Journal of Investigation in Health, Psychology and Education, 15(1), 6, in 2025.
This academic article, published in the European Journal of Investigation in Health, Psychology and Education in 2025, presents a psychological exploration into why patients resist Artificial Intelligence (AI) in healthcare. Authored by Abu Elnasr E. Sobaih, Asma Chaibi, Riadh Brini, and Tamer Mohamed Abdelghani Ibrahim, the research originates from institutions in Saudi Arabia and Tunisia. The study highlights that despite the significant potential of AI to transform healthcare, its acceptance among patients remains limited. The authors note a crucial gap in comprehensive research addressing the specific variables that drive this patient resistance to AI.
The Evolving Landscape of AI in Healthcare
Over the last few decades, digital transformation, significantly accelerated by the COVID-19 pandemic, has advanced rapidly across various sectors, including healthcare. AI in healthcare (AIH) is an evolving technology that enables providers to manage data by mimicking human cognitive roles with enhanced efficiency and throughput. AI encompasses a broad spectrum of technologies and methods that allow machines to perform tasks requiring human intelligence, such as visual and speech recognition, reasoning, and problem-solving. Its projected market size is USD 45.2 billion by 2026, indicating its rapid expansion.
AI’s potential in healthcare is vast, including clinical decision support, risk prediction, reduction of medical errors, healthcare intervention, and productivity enhancement. It improves the accuracy and speed of image review in radiology and pathology, and significantly influences patient care through virtual assistance. Examples of AI’s capabilities include analyzing chest X-ray images more accurately than radiologists, processing millions of images daily to enhance workflow efficiency, providing mental health counseling via chatbots, and enabling patients to self-monitor and diagnose conditions like atrial fibrillation and skin lesions. AI integration has also been shown to significantly lower healthcare expenditures compared to traditional diagnostic approaches.
Challenges and the Problem of Patient Resistance
Despite these substantial benefits, many healthcare organizations face significant hurdles in implementing AI technology. These challenges are organizational, financial, technological, and human. Ethical concerns, such as potential algorithmic biases and job displacement risks, also arise. Effective implementation of AI requires examining users’ attitudes and perceptions, as neglecting patient views can lead to wasted resources and disengaged patients. If patients do not find AI useful, they may prefer traditional physician interactions, leading to underutilization of AI tools. Understanding patient resistance, a critical aspect of consumer behavior, is thus essential for successful AI integration.
The authors identify several gaps in the existing literature:
- Previous studies have focused on the acceptance and resistance of other technologies (e.g., IoT, blockchain) but less on AI technology adoption from the user’s perspective.
- Limited research has adopted a grounded theory approach to evaluate the motives and challenges of AI deployment in healthcare.
- Existing studies on technology acceptance in healthcare have primarily focused on design and implementation from the service provider’s perspective, often neglecting patients’ perceptions and behavioral aspects.
- Earlier adoption models like the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) focused more on initial acceptance and positive factors, often ignoring factors hindering adoption or long-term use. These models were deemed less suitable for understanding resistance, which often stems from contentment with the status quo or conflicts with existing belief systems.
Theoretical Framework and Research Model
To address these gaps, the study applies an extended Ram and Sheth Model (1989), specifically delving into the influences of the Need for Personal Contact (NPC), Perceived Technological Dependence (PTD), and General Skepticism toward AI (GSAI) on patient resistance to AI integration. The research adds to the theoretical understanding of Innovation Resistance Theory (IRT), which posits that resistance is a normal reaction to change and is influenced by psychological factors. The choice to focus on patients’ perspectives is driven by their essential role as end-users in determining the success of AI innovations, providing valuable insights into ethical and privacy aspects.
The study proposes three hypotheses:
- H1: Personal contact has a positive impact on patients’ resistance to use AI in healthcare. This hypothesis reflects that patients who prefer human interaction in healthcare services may be hesitant to use technologically assisted means because they value the human element. Patients often express concerns about AI replacing essential human qualities like emotional understanding, personalized care, and the human touch, seeing trust and face-to-face communication as vital.
- H2: Patients’ perceived technological dependence has a positive effect on their resistance to AI in healthcare. This relates to patients’ concerns about over-reliance on AI, fearing loss of human involvement, risks of technology failures, and erosion of personal control over their care.
- H3: Patients who are more skeptical of AI may be more likely to resist its use in healthcare. This stems from patients’ doubts and lack of confidence in AI, especially concerning the complexity and personal nature of healthcare, leading to a preference for human judgment over machines and fears of errors or misdiagnoses.
Research Design and Methodology
A sequential mixed-method approach was employed, beginning with a qualitative phase to identify adaptable factors in healthcare, followed by a quantitative phase to validate these findings.
- Qualitative Study: Semi-structured interviews were conducted with a diverse sample of 43 individuals in Tunisia during winter 2022/2023, encompassing various genders, ages (21-60 years), and occupations. The interviews explored perceptions of AI in general, its impact on healthcare, and factors affecting AI resistance. Thematic analysis using QSR NVivo software was utilized, revealing “Psychological Factors Affecting Patient Resistance to AI” as a major theme, consisting of the three sub-themes: Need for Personal Contact, Perceived Technological Dependence, and General Skepticism.
- Quantitative Study: An online survey was distributed to a wider audience, with data collected using self-administered and interviewer-administered questionnaires. Non-probability sampling techniques (purposive, convenience, and snowball sampling) were used. Attitudes were measured using a Likert scale, and the questionnaire was translated into French. The sample for the quantitative study included 450 participants, with a near-equal gender distribution (50.7% male, 49.3% female) and diverse age groups and professions, with 56.4% having basic knowledge of AI. The constructs were measured using established scales: Need for Personal Contact (Walker et al., 2002), Perceived Technological Dependence (Charlton, 2002), General Skepticism toward AI (Morel & Pruyn, 2003), and Resistance to Use AI (Hseih & Lin, 2017). Data analysis involved Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM).
Key Findings and Discussion
The analysis of the data strongly supported all three hypotheses, confirming the significant role of the Need for Personal Contact, Perceived Technological Dependence, and General Skepticism toward AI in shaping patients’ resistance to AI in healthcare.
- Need for Personal Contact (NPC) exhibited a notable and positive connection with resistance (β = 0.515, p < 0.001). This highlights that patients who value personal interaction, emotional support, and trust with human healthcare providers are more likely to resist AI, fearing a loss of the “human touch”.
- Perceived Technological Dependence (PTD) displayed a significant relationship with resistance (β = 0.620, p < 0.001). This indicates that patient concerns about over-reliance on AI, fears of system failures, and the potential for AI to undermine human decision-making and patient autonomy contribute to resistance.
- General Skepticism toward AI (GSAI) showed a marked positive association with resistance (β = 0.222, p < 0.001). This finding underscores that patients’ doubts about AI’s promises, its complexity, and their discomfort with machines making health decisions contribute to their reluctance to adopt AI in healthcare.
Implications and Limitations
The findings offer valuable guidance for healthcare administrators and policymakers. Strategies should emphasize preserving human interactions, potentially through hybrid approaches that combine AI efficiency with personalized care. Proactive measures are needed to address concerns about technological dependence through tailored communication and education. Building trust and familiarity with AI applications, possibly through gradual and informed exposure, is crucial to mitigate general skepticism. These insights highlight the need for comprehensive change management strategies that consider the complex psychological factors influencing AI adoption in healthcare settings.
The study acknowledges limitations, including its specific focus on the Tunisian healthcare sector, which may limit generalizability, and its focus solely on direct links between psychological factors and resistance, without exploring mediating or moderating variables. Future research is recommended to replicate these findings in diverse healthcare contexts and investigate the moderating roles of patient demographics like gender, age, and education.
In conclusion, this research provides crucial insights into the psychological factors driving patient resistance to AI in healthcare, offering a nuanced understanding for effective AI integration strategies.
References: Sobaih, 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.
