Artificial Intelligence in Healthcare: Awareness,Perceptions,and Future Perspectives of Palestinian Medical Students and Physicians

Abstract

Background

Artificial intelligence (AI) is transforming healthcare by improving diagnostic accuracy, streamlining workflows, and supporting clinical decision-making. While global adoption is accelerating, successful integration depends on healthcare professionals’ preparedness and acceptance. In low-resource settings such as Palestine, infrastructural limitations and lack of formal AI education may influence perceptions and readiness.

Objective

This study evaluates the awareness, perceptions, and future expectations of Palestinian medical students and physicians regarding AI integration in healthcare. It also explores readiness for adoption and identifies educational gaps to support effective implementation.

Methods

A cross-sectional survey was conducted among 915 participants (689 students, 226 physicians) from five universities and healthcare institutions across the West Bank and Gaza between December 2024 and January 2025. A structured, self-administered questionnaire assessed demographics, AI awareness, perceptions, and future perspectives. Data were analyzed using descriptive statistics. Ethical approval was obtained from Al-Azhar University.

Results

Of the 915 respondents (689 students, 226 physicians), most had limited AI exposure—85.8% never attended AI-related events and 93.3% lacked curriculum coverage. Physicians showed higher event attendance (24.3%) than students (10.9%) (χ²(2) = 25.26, p < 0.001, Cramér’s V = 0.17). While 82.3% were aware of AI’s benefits, 78.4% couldn’t name any medical AI software. Attitudes were generally positive: 59.3% agreed AI improves outcomes, and 83.6% supported formal AI training. Interest in AI careers was expressed by 40.8%, with radiology (54.6%) and health information management (60.6%) seen as key future applications.

Conclusions

Palestinian medical students and physicians show growing interest in AI despite limited formal education and practical exposure. Both groups view AI as a supportive tool rather than a replacement for clinical judgment, while expressing concerns about ethical risks and technical limitations. Tailored educational strategies are essential to bridge knowledge gaps and promote responsible AI integration into medical practice.

Keywords

artificial intelligence palestinian healthcare medical education medical students

Introduction

Artificial intelligence (AI) refers to the capacity of machines to simulate human cognitive functions such as learning, reasoning, and decision-making.^1,2 In healthcare, AI systems are increasingly used to process complex data, support clinical decisions, and automate routine tasks. Applications range from diagnostic imaging and predictive analytics to personalized treatment planning and administrative support.^3,4 The integration of AI into clinical practice has the potential to enhance diagnostic accuracy, reduce human error, and improve overall healthcare efficiency.⁵ However, its successful implementation depends not only on technological advancement but also on the readiness and acceptance of healthcare professionals.^6,7 Recent years have witnessed a surge in AI adoption across medical specialties, driven by the development of machine learning algorithms and large language models capable of outperforming humans in specific tasks.

Despite the growing integration of AI in clinical settings, several concerns persist. Ethical challenges include unclear accountability for AI-driven decisions and limited transparency in patient consent.⁸ Reliance on large-scale health data raises significant data privacy issues, including risks of unauthorized access and ambiguity around data ownership.⁹ Algorithmic bias, stemming from non-representative training data, can lead to unequal diagnostic performance across diverse populations.¹⁰ Moreover, increasing dependence on AI may undermine clinicians’ diagnostic reasoning and compromise the patient–physician relationship by reducing human oversight and empathy.¹¹ These concerns are particularly relevant in low-resource settings, where infrastructural limitations and lack of formal AI training may hinder effective integration.^12-14

Medical education plays a pivotal role in preparing future clinicians to navigate the evolving landscape of AI-enabled healthcare.¹⁵ Studies suggest that while medical students generally express enthusiasm toward AI, they often lack the foundational knowledge and practical skills to use it effectively.^16,17 A global survey by Busch et al, 2024 revealed that over 75% of medical students had received no formal education in AI, despite recognising its importance for their future careers.¹⁸ Similarly, many physicians acknowledged AI’s potential benefits, butalso expressed scepticism about its reliability and impact on clinical autonomy.^19,20 Understanding and addressing these challenges is essential to ensure that AI technologies are implemented safely, equitably, and effectively across diverse healthcare environments.

The literature highlights a growing consensus on the need for structured AI curricula in medical schools and continuing education programs. Scoping reviews emphasize the importance of embedding AI within interdisciplinary, patient-centered frameworks that prioritize ethical awareness and digital competence.^21-23 Institutions such as Harvard Medical School and the American Medical Association have begun integrating AI modules into their training programs, signaling a shift toward proactive educational reform.⁷

The investigation of healthcare professionals’ awareness, perceptions, and readiness to adopt artificial intelligence can be conceptually framed using established technology adoption models. The Technology Acceptance Model (TAM) posits that users’ acceptance of new technologies is primarily determined by perceived usefulness and perceived ease of use, which shape attitudes and behavioral intentions toward adoption.²⁴ Complementing this, the Unified Theory of Acceptance and Use of Technology (UTAUT) integrates social influence, facilitating conditions, and performance expectancy as key determinants of technology uptake.²⁵ These frameworks have been widely applied in healthcare and medical education to understand clinicians’ and students’ engagement with digital health innovations.^24,26,27

In the context of Palestine, the integration of artificial intelligence into healthcare and medical education must be understood against a backdrop of prolonged political instability, movement restrictions, and recurrent conflict, which have profoundly shaped the healthcare system.²⁸ Hospitals and medical schools operate under infrastructural constraints that extend beyond limited funding, including inconsistent electricity supply, restricted access to digital health technologies, shortages of medical equipment, and disrupted training opportunities—particularly in Gaza.^29,30 These conditions place exceptional strain on healthcare professionals and trainees, contributing to workforce burnout, fragmented continuity of care, and limited exposure to emerging technologies.^31,32 At the same time, conflict-related psychosocial stressors affecting both patients and healthcare providers may influence attitudes toward automation, trust in digital systems, and openness to technological innovation.³³ Within this uniquely constrained environment, artificial intelligence holds both promise and risk: while AI may offer tools to optimize efficiency and offset workforce shortages, its successful adoption depends heavily on contextual readiness, ethical safeguards, and targeted education.^34,35

Despite the growing global emphasis on artificial intelligence in healthcare, empirical data on AI awareness and readiness among Palestinian medical students and physicians remain scarce. Understanding how clinicians and future physicians in this conflict-affected, low-resource setting perceive AI is essential for informing context-appropriate educational strategies and promoting equitable, responsible technology integration. Examining their knowledge, attitudes, and concerns can help identify key educational gaps and guide curriculum development, faculty training, and policy planning. Accordingly, this study aims to assess the awareness, perceptions, and future perspectives of Palestinian medical students and physicians regarding the integration of artificial intelligence into healthcare practice.

Objectives

Primary Objective

• To assess the level of awareness and perceptions of artificial intelligence (AI) among Palestinian medical students and physicians regarding its current and future integration into healthcare practice.

Secondary Objectives

• To compare awareness, attitudes, and perceptions of AI between medical students and practicing physicians.

• To evaluate participants’ exposure to formal AI education and training within medical curricula and professional development programs.

• To identify the extent of the knowledge–practice gap, particularly the ability to recognize specific AI applications or software used in medicine.

• To explore participants’ future expectations, ethical concerns, and interest in AI-related careers.

Methods

Study Design and Participants

This study employed a cross-sectional survey design to assess the awareness, perceptions, and future perspectives of Palestinian medical students and physicians regarding the integration of AI in healthcare practice. The target population for this study included medical students enrolled in Palestinian universities, as well as practicing physicians working in governmental and private healthcare institutions across the West Bank and Gaza Strip. Participants were recruited from major medical schools and hospitals to ensure a representative sample of the Palestinian medical community, capturing a broad spectrum of experiences, educational backgrounds, and clinical exposure. Individuals who were not currently enrolled in medical training or not actively practicing medicine, as well as incomplete questionnaire responses, were excluded from the analysis. The reporting of this cross-sectional study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement, and the completed STROBE checklist for cross-sectional studies is provided as a supplementary file (Supplementary File 1).³⁶

Data Collection

The data was collected during the period of December 2024 to January 2025. The questionnaire was distributed electronically via institutional mailing lists, social media platforms, and professional networks to ensure broad reach and accessibility. To ensure validity and reliability, content validity was established through expert review, and a pilot study was conducted with 30 participants to assess clarity, relevance, and reliability. Participants were permitted to review and revise their responses before final submission to enhance data accuracy. Because the survey link was disseminated through multiple open channels, the total number of individuals who were exposed to the survey invitation could not be determined; therefore, a response rate could not be calculated. To mitigate ambiguity, the study focused on descriptive analysis of the responses obtained from participants who completed the questionnaire within the study period.

Sampling

A total of 915 participants were recruited using a non-probability convenience sampling approach, comprising physicians from various healthcare centers and medical students from five accredited medical schools across Palestine. The sample size was calculated a priori assuming a 95% confidence level, a margin of error of 5%, and an expected proportion of 50%, given the lack of prior data on the outcomes of interest. This yielded an initial required sample size of 384 participants. A finite population correction was then applied based on an estimated target population of approximately 18,000 individuals, resulting in a final required sample size of 376 participants. The final achieved sample size substantially exceeded the minimum required, thereby increasing the precision of estimates and strengthening the robustness of the study findings. To minimize potential sources of bias, the questionnaire was anonymized, participation was voluntary, standardized instructions were provided to all participants, eligibility criteria were clearly defined, questionnaires with incomplete responses were excluded prior to analysis.

Survey Instrument

A structured, self-administered questionnaire was developed based on a comprehensive literature review and expert input from professionals in medical education and artificial intelligence.^10,16,37-40 In designing the questionnaire, we considered also incorporating elements from the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), which are widely used frameworks in behavioral and implementation science to assess technology adoption. These models have been successfully applied in healthcare settings to evaluate clinicians’ and students’ readiness to adopt emerging technologies, including AI.^25,27,41 The questionnaire was initially developed in English, then translated into Arabic by two bilingual translators. It was subsequently back-translated into English by two independent translators to ensure accuracy. After resolving discrepancies through consensus, the final validated version used for data collection was in Arabic. The questionnaire consisted of four main sections: Demographics, Awareness of AI, Perceptions and Attitudes, and Future Perspectives.

The Demographics section collected data on age, gender, academic/professional level, institution, and region. The Awareness of AI section assessed participants’ exposure to AI through questions about attending seminars or courses, curriculum inclusion, and self-rated knowledge of AI concepts, applications, barriers, ethical concerns, benefits, and awareness of AI software used in medicine. The Perceptions and Attitudes section explored beliefs regarding AI’s potential to identify patterns in large-scale data, improve health outcomes, enhance diagnostic accuracy and efficiency, and reduce human errors, as well as concerns about AI-induced bias in medical decision-making. The Future Perspectives section evaluated expectations for AI integration in healthcare, including agreement with the perceived need for formal AI training programs in medical institutions, beliefs about AI becoming essential in future practice, and personal interest in pursuing AI-related careers. Internal consistency was assessed using Cronbach’s alpha for the multi-item scales within the Attitudes, Perception, and Future Perspectives subsections, yielding alpha coefficients of 0.71, 0.73, and 0.80, respectively, indicating acceptable internal consistency. A threshold of ≥0.7 was considered acceptable.⁴²

Statistical Analysis

All statistical analyses were performed using SPSS software. Descriptive statistics, including frequencies, percentages, means, and standard deviations, were used to summarize participants’ demographic characteristics and questionnaire responses. To evaluate differences between medical students and physicians, comparative inferential analyses were undertaken. Chi-square tests were used to compare categorical variables, with Cramér’s V reported as a measure of effect size, while independent-samples t-tests were applied to compare continuous variables such as age. A two-sided p-value of < 0.05 was considered statistically significant. Missing values were handled using available-case analysis without imputation.

Ethical Considerations

Ethical approval for the study was obtained from the Research Ethics Committee of Al-Azhar University (Ethical Approval Reference No. AUG-SR-2024-0128). The study was conducted in accordance with institutional ethical standards and the Declaration of Helsinki. Written informed consent was obtained electronically from all participants prior to participation. The survey was designed so that study information was presented first, and access to the questionnaire items was granted only after participants explicitly indicated their consent to participate. The procedure for obtaining this informed consent was reviewed and approved by the Research Ethics Committee. To maintain confidentiality, all data were anonymised, and records were securely stored in password-protected files accessible only to the research team.

Results

Demographics and Participants’ Background

The total number of completed responses was 915, which included 689 medical students and 226 physicians. The overall mean age of respondents was 28 years (SD = 12.76). The mean age of the medical students was 21(SD=1.67) and the physicians was 48(SD=11.52)(p < 0.001). Male participants constituted around half of the respondents (51.48%). Among physicians, 83.63% were males, compared to 40.93%among medical students (p < 0.001). Most of the physicians were consultants (65.49%). Meanwhile, most of the medical students were from Al-Azhar University, Gaza (65.31%), and in their third year (47.17%)(p < 0.001). The sample characteristics are summarised in Table 1.

Table 1.

Study Sample Characteristics

			p-value
Age(years), mean(SD)			p < 0.001
Total (n=915)	27.87 (12.76)
Medical students (n=689)	21.40 (1.67)
Physicians (n=226)	47.68 (11.52)
Gender, n(%)			p < 0.001
Total (n=915)	Female	444 (48.52)
Total (n=915)	Male	471 (51.48)
Medical students (n=689)	Female	407 (59.07)
Medical students (n=689)	Male	282 (40.93)
Physicians (n=226)	Female	37 (16.37)
Physicians (n=226)	Male	189 (83.63)
Occupation, n(%)			p < 0.001
Consultant	148 (16.17)
Resident	23 (2.51)
General Physician	50 (5.47)
Intern	5 (0.55)
Medical Student	689 (75.30)
University, n(%)			p < 0.001
Al-Azhar University Gaza	450 (65.31)
Al-Quds University	140 (20.32)
An-Najah National University	1 (0.15)
Hebron University	53 (7.69)
Islamic University of Gaza	20 (2.90)
Palestine Polytechnic University	25 (3.63)
Academic year, n(%)			p < 0.001
First Year	41 (5.95)
Second Year	58 (8.42)
Third year	325 (47.17)
Fourth year	52 (7.55)
Fifth year	90 (13.06)
Sixth year	123 (17.85)

General Knowledge of AI

The majority of the respondents never attended events related to AI in healthcare (85.79%), and their curriculum doesn’t include an AI introduction (93.33%). AI-related event attendance rate was 24.34% among physicians and 10.89% among the medical students (χ²(2) = 25.26, p < 0.001, Cramér’s V = 0.17). 94.48% of medical students’ and 89.82% of physicians’ curricula don’t include AI introduction (χ²(2) = 5.94, p =0.02, Cramér’s V = 0.08). Notably, 28.09% of participants disclosed being fully unaware or unaware of possible AI applications in medicine, including 25.83% and 34.96% of medical students and physicians, respectively (χ²(2) = 17.04, p < 0.001, Cramér’s V = 0.14). Regarding awareness of AI-related barriers, 35.52% of the respondents reported being fully aware or aware with respect to AI-related barriers, while 34.83% of medical students and 37.17% of physicians were neutral. (Table 2).

Table 2.

General Knowledge of AI

	Total (N=915)	Medical students (N=689)	Physicians (N=226)	p-value	χ²(2)	Cramér’s V
Have you ever attended any seminar, lecture, or course on AI in healthcare? n(%)				p < 0.001	25.26	0.17
Yes	130 (14.2)	75 (10.89)	55 (24.34)
No	785 (85.8)	614 (89.11)	171 (75.64)
Does your curriculum include an introduction to AI and its applications in healthcare? n(%)				0.02	5.94	0.08
Yes	61 (6.67)	38 (5.52)	23 (10.18)
No	854 (93.33)	651 (94.48)	203 (89.82)
How would you rate your knowledge of possible AI concepts and their applications in healthcare? n(%)				p < 0.001	17.04	0.14
Fully Aware	37 (4.04)	30 (4.35)	7 (3.1)
Aware	313 (34.21)	235 (34.11)	78 (34.51)
Neutral	308 (33.66)	246 (35.7)	62 (27.43)
Unaware	196 (21.42)	144 (20.9)	52 (23.01)
Fully unaware	61 (6.67)	34 (4.94)	27 (11.95)
How would you rate your knowledge of barriers to applying AI in healthcare? n(%)				0.32	4.70	0.07
Fully Aware	50 (5.46)	37 (5.37)	13 (5.75)
Aware	275 (30.05)	212 (30.77)	63 (27.88)
Neutral	324 (35.41)	240 (34.83)	84 (37.17)
Unaware	229 (25.03)	177 (25.69)	52 (23.01)
Fully unaware	37 (4.05)	23 (3.34)	14 (6.19)
How would you rate your knowledge of the ethical concerns related to the use of AI in healthcare? n(%)				0.38	4.17	0.07
Fully Aware	180 (19.67)	133 (19.3)	47 (20.8)
Aware	531 (58.03)	394 (57.18)	137 (60.62)
Neutral	150 (16.39)	117 (16.98)	33 (14.6)
Unaware	44 (4.82)	38 (5.52)	6 (2.65)
Fully unaware	10 (1.09)	7 (1.02)	3 (1.33)
How would you rate your knowledge of the benefits related to the use of AI in healthcare? n(%)				0.24	5.45	0.08
Fully Aware	228 (24.92)	174 (25.25)	54 (23.89)
Aware	525 (57.37)	385 (55.88)	140 (61.95)
Neutral	145 (15.85)	114 (16.54)	31 (13.72)
Unaware	12 (1.31)	11 (1.6)	1 (0.44)
Fully unaware	5 (0.55)	5 (0.73)	0 (0)
Are you aware of any AI software programs in medicine? n(%)				0.19	1.74	0.04
Yes	198 (21.64)	142 (20.61)	56 (24.78)
No	717 (78.36)	547 (79.39)	170 (75.22)

Attitudes Toward AI in Healthcare

Participants expressed generally optimistic attitudes toward the capabilities of artificial intelligence in healthcare. A majority agreed that AI can significantly improve healthcare outcomes (59.34%), enhance the accuracy of medical diagnoses (50.67%), and reduce human error in clinical practice (59.02%). Additionally, 64.59% believed AI contributes to advancing medical research. However, only 17.18% agreed that AI could be more accurate than physicians, reflecting a cautious stance toward fully autonomous systems. Notably, 41.75% acknowledged that AI might introduce bias into medical decision-making. Figure 1A and B illustrate the comparative responses between medical students and physicians across these dimensions.

Figure 1.

(A) Attitude towards AI in healthcare among medical students. (B) Attitude towards AI in healthcare among physicians

Future Perspectives and Ethical Considerations

The majority of respondents revealed consciousness of the importance of incorporating formal AI training into hospitals’ and medical schools’ programs, with the majority (83.61%) agreeing or strongly agreeing. While 14.80% of the medical students are neutral, and none of the physicians strongly disagree (0.00%), (χ²(2) = 11.43, p = 0.02, Cramér’s V = 0.11). The majority of the participants opposed the notion that AI could replace doctors in the healthcare system, with 54.10% strongly disagreeing or disagreeing, while 21.77% of medical students strongly agree or agree, and 27.43% of physicians were neutral. Although 51.58% of the respondents agreed that AI will become an essential part of future medical practice, 45.28% of medical students were uncertain, and 7.08% of the physicians think that AI is not an essential part of future medical practice (χ²(2) = 7.65, p = 0.02, Cramér’s V = 0.09). Finally, 40.77% of the respondents expressed interest in pursuing AI-related careers, whereas 39.04% of medical students were unsure, and 15.04% of the physicians declined (χ²(2) = 35.38, p < 0.001, Cramér’s V = 0.20) (Table 3).

Table 3.

Future perspectives and ethical considerations

	Total (N=915)	Medical students (N=689)	Physicians (N=226)	p-value	χ²(2)	Cramér’s V
To what extent do you agree or disagree: ‘Medical schools and hospitals will need formal AI training programs’? n(%)				0.02	11.43	0.11
Strongly Agree	279 (30.49)	211 (30.63)	68 (30.09)
Agree	486 (53.11)	350 (50.8)	136 (60.18)
Neutral	120 (13.11)	102 (14.8)	18 (7.96)
Disagree	26 (2.85)	22 (3.19)	4 (1.77)
Strongly Disagree	4 (0.44)	4 (0.58)	0 (0)
To what extent do you agree or disagree: ‘AI will be able to replace doctors in the healthcare system?n(%)				0.43	3.82	0.07
Strongly Agree	47 (5.14)	39 (5.66)	8 (3.54)
Agree	139 (15.19)	111 (16.11)	28 (12.39)
Neutral	234 (25.57)	172 (24.96)	62 (27.43)
Disagree	191 (20.87)	141 (20.47)	50 (22.12)
Strongly Disagree	304 (33.23)	226 (32.8)	78 (34.52)
Do you believe AI will become an essential part of future medical practice?n(%)				0.02	7.65	0.09
Yes	472 (51.59)	341 (49.49)	131 (57.96)
No	52 (5.68)	36 (5.23)	16 (7.08)
Maybe	391 (42.73)	312 (45.28)	79 (34.96)
Are you personally interested in pursuing a career or specialization involving AI in healthcare?n(%)				p < 0.001	35.38	0.20
Yes	373 (40.77)	243 (35.27)	130 (57.52)
No	211 (23.06)	177 (25.69)	34 (15.04)
Maybe	331 (36.17)	269 (39.04)	62 (27.44)

The analysis of respondents’ opinions on the potential future application of AI in the Palestinian healthcare system revealed that radiology was the most frequently cited field among physicians (54.6%), followed by robotic surgery (50%). Among medical students, radiology and robotic surgery were also the most cited areas, though at lower rates—46.3% and 23.4%, respectively. On the other hand, the most commonly reported AI application by physicians was health information management (HIM) (60.60%), followed by radiology (54.90%). Figure 2.

Figure 2.

Distribution of Respondents’ Opinions on AI Applications in Healthcare Categories

Discussion

This cross-sectional study provides valuable insights into the awareness, perceptions, and future perspectives of Palestinian medical students and physicians regarding the integration of AI in healthcare practice. The findings reveal a complex landscape of optimism, limited exposure, and cautious anticipation, reflecting both the promise and challenges of AI adoption in a developing healthcare system.

A key finding of this study is the clear knowledge–practice gap between participants’ high self-reported awareness of artificial intelligence and their limited ability to identify concrete AI applications in clinical practice. Although most respondents recognized AI’s potential benefits and ethical implications, 78.4% were unable to name any AI-based medical software, indicating that this awareness is largely conceptual rather than practical. These findings are consistent with recent studies conducted in Palestinian institutions, which have documented challenges in clinical learning environments, disrupted curricula, and limited opportunities for applied digital training under conditions of prolonged political instability and resource constraints.^43,44 Such educational limitations may contribute to gaps in preparedness, confidence, and practical readiness to engage with emerging technologies, including AI. Similar observations have been reported in other low-resource and conflict-affected settings, where AI education remains inconsistently integrated into medical training programs and is often constrained by infrastructural and institutional barriers.^6,45,46 This gap has important educational implications, as superficial familiarity may limit clinicians’ ability to critically evaluate AI tools, integrate them safely into clinical workflows, and recognize their limitations or biases.^10,17 Addressing this gap through practice-oriented, competency-based AI education is therefore essential to translate growing interest in AI into meaningful clinical competence.^6,7,45

Despite limited formal exposure to AI, most respondents in both groups demonstrated awareness of its ethical implications and potential healthcare benefits. This suggests a growing conceptual understanding of AI’s implications, likely influenced by global discourse and informal sources such as media and peer discussions.⁴⁷ However, this self-perceived awareness contrasts sharply with the finding that 78.36% of respondents were unfamiliar with any existing AI software used in medicine. This disconnect highlights a critical gap between theoretical knowledge and practical familiarity. This lack of exposure may hinder the ability of future practitioners to critically evaluate and effectively utilize AI technologies. Similar patterns have been observed across multiple studies. Evidence from systematic reviews and cross-sectional surveys showed that medical students demonstrate positive attitudes toward AI and recognize its ethical implications, despite limited formal training and technical knowledge.^17,23 For example, a large multinational survey of Arab medical students found that while over 90% had not received formal AI training, the majority still believed AI would transform medicine.¹⁶ Likewise, other studies observed that students often learned about AI from media rather than curricula, leaving them unprepared to critically appraise or effectively apply AI.^47,48 Together, these findings underscore that limited foundational knowledge and lack of structured education hinder the ability of future practitioners to effectively utilize AI in clinical settings, which further reinforces limited readiness.

While ethical considerations and potential benefits of AI appear to be gaining traction, the practical challenges of AI adoption remains less understood. Both student and physician expressed neutrality towards the issue, suggesting a shared uncertainty or limited exposure. This knowledge gap could indicate limited readiness among both groups to anticipate and mitigate the complexities of AI implementation, thereby undermining efforts toward responsible and effective integration of AI into healthcare practice.

While participants maintained a generally neutral stance toward the challenges of AI in healthcare, 41.75% acknowledged that it might introduce bias into medical decision-making. This awareness of potential ethical and technical risks naturally connects to their rejection of the idea that AI could replace doctors, with 54.10% strongly disagreeing or disagreeing with such a notion. These perspectives highlight that AI is primarily viewed as a supportive tool rather than a substitute for human expertise, emphasizing the continued centrality of clinical judgment. This cautious orientation underscores the importance of transparency, algorithmic fairness, and clinician oversight in AI-driven healthcare systems. Subtle differences between medical students and physicians—likely shaped by clinical experience, exposure to AI technologies, and educational background—further reflect this nuanced perception. Similar global findings reinforce this trend: a systematic review reported that only about 15% of medical students and physicians considered AI more accurate than doctors, despite recognizing its efficiency in certain tasks.¹⁰ Likewise, a Russian survey revealed that approximately 20% of physicians believed AI’s diagnostic abilities exceeded those of human clinicians, while the majority rejected the notion of replacing doctors.⁴⁹

Medical students and physicians share similar views on the need for formal AI education and skepticism about AI replacing doctors. This is consistent with the findings of Busch et al, 2024, who found that a majority of medical students across 48 countries expressed interest in AI education, though many felt unprepared to use it professionally.¹⁸ Another study reported that 77% of respondents were optimistic about AI’s future in medicine, but 68% disagreed with the idea that AI could replace physicians, preferring it as a supportive tool.¹⁰ However, physicians are slightly more confident in AI becoming essential to future practice and are more personally interested in AI specialization. Similarly, physicians in a related study showed greater enthusiasm for AI, suggesting that familiarity plays a key role in shaping attitudes.⁵⁰ This may be attributed to their practical experience and awareness of AI’s current capabilities and limitations. In contrast, students, while supportive of AI’s potential, show more uncertainty—likely due to limited exposure and training. These differences highlight the importance of tailoring AI education to each group’s needs: foundational and conceptual for students, and practical and ethical for physicians. Bridging this gap will be crucial for fostering informed, confident adoption of AI in healthcare.

Beyond resource limitations, the findings of this study must be interpreted within the context of Palestine’s protracted political instability and chronic disruption of healthcare delivery.⁵¹ Decades of movement restrictions, recurrent conflict, and fragmentation of health governance have produced a healthcare system characterized by uneven service access, strained human resources, and limited continuity of training—particularly in Gaza.⁵² These conditions fundamentally shape how medical students and physicians perceive technological innovation, including artificial intelligence.⁵³ Recent studies among Palestinian healthcare workers have documented high levels of occupational stress, burnout, and systemic burden, which may further influence perceptions of technological change and readiness for innovation.^44,54

In conflict-affected health systems, digital technologies may be viewed simultaneously as potential solutions and as sources of added vulnerability.⁵⁵ On one hand, AI-enabled tools have the potential to enhance efficiency, support clinical decision-making in workforce-constrained environments, and mitigate access gaps caused by restricted mobility.⁵⁶ On the other hand, persistent electricity shortages, unreliable internet connectivity, limited interoperability between institutions, and the absence of robust regulatory frameworks create significant obstacles to AI implementation.³⁴ These realities likely contribute to the cautious optimism observed among respondents, who largely viewed AI as a supportive tool rather than a replacement for clinicians.^13,57

Moreover, prolonged exposure to conflict-related psychosocial stress and institutional instability may influence trust in digital systems and shape ethical concerns surrounding automation, data governance, and accountability. In this setting, limited exposure to functional AI systems compounds uncertainty, reinforcing the gap between conceptual awareness and practical readiness identified in our results. A parallel can be drawn with recent research among Palestinian nurses, where concerns about AI adoption were similarly shaped by limited training opportunities and systemic pressures rather than opposition to innovation, suggesting that such apprehensions may be shared across healthcare professions.⁵⁸ By explicitly situating these findings within a conflict-affected, digitally constrained healthcare environment, this study extends the global discourse on AI in medical education beyond high-income and stable low-resource contexts, underscoring the need for tailored educational models and context-sensitive implementation strategies in fragile health systems.^55,59 Lessons from the Palestinian context may inform AI education and implementation strategies in other conflict-affected and politically constrained health systems, where structural instability rather than technological skepticism is the primary barrier to adoption.

Recommendations

Based on the study findings and the contextual constraints of the Palestinian healthcare system, recommendations for AI education should be pragmatic, phased, and locally relevant rather than aspirational. Given limitations in digital infrastructure, initial AI training modules should prioritize low-bandwidth, rule-based or decision-support applications relevant to high-burden clinical areas such as primary care triage, chronic disease management, and laboratory decision support, rather than data-intensive imaging or robotics-driven systems.

At the undergraduate level, AI education may be most feasible when integrated into existing courses—such as public health, medical ethics, and evidence-based medicine—focusing on foundational concepts, ethical implications, and critical appraisal of AI tools rather than technical development. For practicing physicians, short, case-based continuing medical education workshops emphasizing safe use, limitations, and clinical oversight of AI systems may be more appropriate than advanced programming-oriented training. At the institutional level, collaboration between medical schools, healthcare facilities, and regulatory bodies is needed to develop context-sensitive guidelines addressing data governance, accountability, and patient safety in AI-assisted care. Collectively, these targeted strategies can promote responsible AI literacy while avoiding misalignment between technological ambition and on-the-ground feasibility in conflict-affected healthcare settings.

Limitations

Several limitations should be considered when interpreting the findings of this study. First, the cross-sectional and primarily descriptive design precludes causal inference regarding the relationships between observed variables and limits interpretation to associations identified at a single time point. Second, the use of convenience (non-probability) sampling may restrict the generalizability of the findings. In particular, the sample was heavily weighted toward medical students and participants recruited from a single institution, which may not fully represent the broader population of Palestinian medical students and physicians across different regions, training environments, and institutional contexts. These factors increase the potential for selection bias and warrant caution in extrapolating the findings beyond the study sample.

Third, the study relied on self-reported measures, which are subject to recall bias and reporting bias; this is especially relevant in conflict-affected settings, where distress, trauma, and social desirability may influence responses, and where the study itself suggests that self-reported perceptions may not always reliably reflect objective conditions. Finally, the absence of qualitative data limits the ability to explore the underlying reasons and contextual mechanisms behind the observed quantitative patterns, particularly regarding healthcare access, displacement experiences, and coping strategies. Future research using longitudinal, probability-based, and mixed-methods designs could help overcome these limitations and offer deeper insight into healthcare professionals’ readiness to adopt artificial intelligence.

Conclusions

This study highlights the perceived awareness, attitudes, and readiness of Palestinian medical students and physicians toward the integration of artificial intelligence into healthcare. While respondents generally expressed positive perceptions and interest in AI, the findings indicate limited formal training and practical exposure, reflecting a partial and developing readiness rather than full preparedness for implementation. These results underscore the importance of strengthening context-appropriate AI education and institutional support to bridge the gap between growing interest and realistic adoption capacity in resource-constrained and conflict-affected healthcare settings.

Supplemental Material

Supplemental Material - Artificial Intelligence in Healthcare: Awareness, Perceptions, and Future Perspectives of Palestinian Medical Students and Physicians

Supplemental Material for Artificial Intelligence in Healthcare: Awareness, Perceptions, and Future Perspectives of Palestinian Medical Students and Physicians by Yaser Hamam, Maha AbuZarifa, Alaa Abushahla, Mohamed R Zughbur, Majd Hamam, Nada Hamam, Ola Abuolwan and Sulaiman Ewaida.

Supplemental Material

Supplemental Material - Artificial Intelligence in Healthcare: Awareness, Perceptions, and Future Perspectives of Palestinian Medical Students and Physicians

Footnotes

Acknowledgement

The authors have no acknowledgements to declare.

ORCID iDs

Yaser Hamam

Maha AbuZarifa

Ethical Considerations

Ethical approval for the study was granted by the Research Ethics Committee of Al-Azhar University, ensuring full compliance with ethical guidelines for research involving human participants.

Consent to Participate

Informed consent was obtained from all participants prior to participation after explaining the study objectives and procedures. Participation was voluntary, and confidentiality and anonymity were ensured.

Consent for Publication

Informed consent for publication was obtained from all participants or their legally authorized representatives. The consent forms are held by the authors and are available upon reasonable request.

Author Contributions

Y.H.1 contributed to study conceptualisation, literature review, study design, tool development, figure preparation, and led the writing and revision of the manuscript. M.Z.3 contributed to conceptualisation, literature review, and data collection. M.A.2 contributed to conceptualisation, literature review, data analysis and interpretation, and manuscript writing. A.A.1 contributed to conceptualisation, literature review, and manuscript writing and review. M.H.4 contributed to study design, tool development, and data analysis and interpretation. N.H.5 contributed to study design, tool development, and data analysis and interpretation. O.A.6 contributed to data collection, data curation and revision of the manuscript. S.E.7 contributed to data curation, figure preparation and literature review. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

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