Awareness, Attitude, and Readiness Toward Artificial Intelligence and Digital Health Technologies Among Pharmacy Students as Future Healthcare Professionals: A Cross-Sectional Study

Artificial Intelligence (AI), simply put, the ability of a computer-based system to learn, reason and recognize patterns—processes that have traditionally been the domain of human intelligence, is now being woven into the fabric of modern healthcare delivery systems. In the context of pharmacy practice, AI-driven solutions are helping to support healthcare practitioners by assisting with drug interaction alerts, automated dispensing systems, AI-driven adherence reminders, and clinical decision support systems. The pharmacist's job responsibilities have transcended the role of a mere dispenser to include electronic health records (EHRs), telemedicine, mobile health (mHealth) applications, telepharmacy services and real time data analysis. The transformation is being accelerated by national initiatives like Ayushman Bharat Digital Mission (ABDM) and the National Digital Health Mission (NDHM) to create a federated digital health infrastructure, which are pushing pharmacists towards working amidst more data-driven systems.^3,4 Globally, digital health competence is also officially included in the Education Development Goals for Pharmacy Education (FIP) 2020 and the WHO Governance Framework for AI in Health (2021), which highlight the importance of a digitally competent workforce. ^4,5

Review of Literature

To set the present study in the context of the global AI readiness for health care and pharmacy education, a literature review was conducted for the last seven years (2018–2025). The majority of international knowledge-attitude-practice (KAP) studies are from Sub Saharan Africa, the Middle East and Southeast Asia. In Zambia, Mudenda et al. (2024) found that 71.3% of pharmacy students had limited knowledge of AI and negative attitudes towards its use despite reporting frequent self-reported usage.⁶ In Saudi Arabia, Alhur et al. (2025) indicated that 79.8% of healthcare professionals had received no formal training in AI, with 72.9% stating they considered its impact on health outcomes positive.⁷  In the Indian context, Tezpal et al. (2024) reported that 79.8% of healthcare professionals had not had any formal training sessions on AI and 72.9% had a positive view on its impact on health outcomes.⁸

 The articles reviewed generally share positive sentiments and awareness of AI, but there are no quantitative measures of readiness validated within this population. This is the first study that (i) has developed a composite Readiness Index for all the pharmacy programmes in India, (ii) has compared the Readiness Index of the programmes across one another D.Pharm, B.Pharm, Pharm.D, and M.Pharm. On the other hand, the second cohort of pharmacy students analysed the association between formal educational AI exposure and preparedness (iii) or the third cohort analysed the association between formal education exposure and readiness in Indian pharmacy students. The present study fills these three gaps by analyzing the readiness of 175 students from four Pharmacy programmes and creating a programme specific composite Readiness Index validated.

 

 

Figure 1: Gender, Programme, and Institution-type Distribution of Participants (N=175)

 

Figure 2: AI/Digital Health Workshop Attendance Among Participants (N=175)

 

Figure 3: Overall Mean Domain Scores with Standard Deviation Error Bars (N=175). The dashed line marks the High Readiness threshold (3.5).

 

Figure 4: Item-wise Awareness Mean Scores (N=175). Dashed line marks the 3.5 threshold.

 

Figure 5: Item-wise Attitude Mean Scores (N=175). All items exceed the High threshold (3.5).

 

 

Figure 6: Overall and Programme-wise Readiness Level Distribution (N=175)

 

Figure 7: Programme-wise Comparison of Awareness, Attitude, and Readiness Scores (N=175)

 

Figure 8: Pearson’s Correlation Heatmap Among Domain Scores. Darker shading indicates stronger correlation.

 

Figure 9: Educational Exposure Response Distribution by Item (N=175). SD=Strongly Disagree, D=Disagree, N=Neutral, A=Agree, SA=Strongly Agree.

DISCUSSION

The study is one of the first studies to measure AI and digital health readiness across four levels of the pharmacy programmes within a single national cohort of 175 Indian pharmacy students. In general, students exhibited moderate to high levels of awareness, positive attitudes and mainly high readiness (77.7%) to the AI and digital health technologies.

The overall Awareness score (3.97±0.78) indicates medium awareness of AI applications in healthcare. The Attitude score (4.10) was the highest of the three domains, suggesting a strong positive attitude towards AI adoption. This is an opposite result to that of Mudenda et al. (2024) that pharmacy students in Zambia (71.3%) have negative attitudes towards technology; this difference could be attributed to the differences in educational infrastructure, access to digital technology, and the socio-technological environment across Sub-Saharan Africa and India. The present results are more consistent with the results of Alhur et al. (2025) in Saudi Arabia, who found mostly positive attitudes towards AI and Busch et al. (2024) in their multinational sample who found mostly positive attitudes towards AI. ^7,10

Cross-section analysis of the relationship between attitude and readiness yielded a positive correlation (r=0.755, p<0.001) and it is recommended that attitude-centred educational programs (e.g. case studies, exposure to practising professionals, and AI success stories in the field of Pharmacy) be incorporated alongside knowledge-based education to improve practical readiness. Motivation and fact therefore must be included in the curriculum design.

The observed programme-wise gradient (Pharm.D > M.Pharm > D.Pharm > B.Pharm) is consistent with the clinical orientation of the Pharm.D curriculum, which affords frequent exposure to patient-care systems, drug databases, and EHRs. This pattern parallels the findings of Alhur et al. (2025), who identified year of study as a key determinant of AI familiarity and adoption intention among pharmacy students. The higher proportion of Moderate Readiness among B.Pharm students (25.9%) relative to Pharm.D students (6.7%) strengthens the argument for introducing AI and digital health content earlier in the undergraduate curriculum, prior to the clinical training phase.

Contrary to the results of Mudenda et al. (2024) and Alhur et al. (2025), women and men did not significantly differ in their AI-related knowledge, attitude and practice in the present study, which can be attributed to relatively homogeneous digital environment of Indian pharmacy students where knowledge, attitude and practices related to using smartphones, social media and information seeking is mostly independent of gender.^6,7  This result could also be due to selection bias, because those who agree to take part in an AI-literacy survey might already constitute a sub-sample with higher levels of technology engagement. The higher preparedness scores of the workshop participants when compared with those who did not attend the workshop (4.17 vs. 3.94) confirms the findings of Tezpal et al.2014 who reported a similar association between the exposure to structured AI-course and preparedness (r=0.426, p<0.001)⁸ thereby reinforcing the importance of implementing AI and digital health workshops, seminars, or short courses as a complementary part of the pharmacy curriculum, which was strongly supported by 72.0% of the surveyed students, compared to the formal incorporation of these courses.

 In addition to the empirical results, this study contributes to the methodology as the composite Readiness Index can be used as a standardised, reusable tool to measure programme and institutional-level readiness and to monitor progress after educational interventions.

CONCLUSION

The study, which was cross-sectional, included 175 students from four levels of pharmacy programmes across India and evaluated their awareness, attitude and preparedness to AI and digital health technologies using a newly created composite Readiness Index. The key findings are: (i) awareness was moderate to high, with the highest scores in healthcare and pharmacy practice with digital tools (mean, 3.97±0.78); (ii) a strong endorsement of attitudes at all programme levels (mean, 4.10±0.81); and (iii) a strong positive association between workshop attendance and readiness (4.17 vs. 3.94). The overall findings show a positive outlook of the Indian pharmacy students towards incorporating AI and digital health into their education. There are still areas lacking in knowledge, however, such as telepharmacy, EHRs and more sophisticated applications of AI, most prominently among B.Pharm students, with focus on priority areas of curricular intervention. The created Readiness Index in this study is a legitimate and reusable indicator to be used in long term evaluation and institutional comparison of educational programs on AI in Pharmacy programs.

Recommendations
1. The Pharmacy Council of India (PCI) should promote curricular integration of a dedicated course on AI and digital health technologies across the B.Pharm, D.Pharm, Pharm.D, and M.Pharm curricula, incorporating programme-specific content such as dispensing automation for D.Pharm, AI applications in drug discovery for B.Pharm, clinical decision support for Pharm.D, and pharmacovigilance analytics for M.Pharm.

2. Institutions should introduce a foundational AI and Digital Health Literacy course for first-year students across all programmes, covering AI fundamentals, EHRs, telemedicine, telepharmacy, and AI ethics.

3. Faculty should be encouraged to collaborate across disciplines — pharmacy, computer science, and health informatics — to develop blended-learning modules incorporating AI simulations and case-based digital health instruction.

4. Institutions should invest in dedicated digital-health laboratories equipped with EHR simulation platforms, AI-based drug-interaction tools, and telepharmacy practice modules.

5. Faculty development programmes should be implemented to strengthen educator competency in AI applications relevant to pharmacy practice.

6. AI-related workshops, webinars, hackathons, and industry–academia collaborative projects should be incorporated into the academic calendar as a complement to formal coursework

Limitations

Because of the cross-sectional design, it is not possible to draw causal conclusions about the relationship between readiness and educational exposure; factors other than those examined may be acting as confounding factors. The results may not be generalisable to other parts of India because the sampling was done from a convenience sample and the study was conducted in a geographically limited area of Pune, Maharashtra. The data is self-reported, which can lead to social-desirability bias, causing people to report their attitudes and willingness to adopt AI to be higher than they truly are. Construct validation of the Readiness Index with objective measures of clinical or research use of AI-tools is warranted, as the Readiness Index showed good internal consistency (α=0.84). Finally, the relatively small subgroup sizes for D.Pharm, M.Pharm, and Pharm.D (n=30 each) constrain the statistical power of inter-programme comparisons.

Future Scope:

Causal evidence for the effectiveness of educational interventions on changes in Awareness, Attitude, and Readiness Index scores would come from structured curricular interventions focused on AI and followed up with longitudinal studies. The study would be more generalizable to the larger, more diverse sample of Indian students in their pharmacy studies if performed in multi-institutional and multi-state studies. Qualitative research would be useful to understand the reasons for reduced preparedness within sub groups and could be complementary. Faculty readiness assessment and aptitude assessment of the students in AI and digital health can be conducted in parallel to find further gaps in the educational system. Additional construct validation with objective behavioral measures (e.g. frequency and confidence of using the EHR or interaction with clinical decision support systems) would increase the construct validity of the Readiness Index. A comparative analysis of the knowledge and practice gap in this could be further illumination between the knowledge and practice gap by comparing pharmacy students with practising pharmacists.

FUNDING

No external funding was received. This study was conducted as part of the Final Year B.Pharm Project at Dr. D. Y. Patil College of Pharmacy, Akurdi, Pune.

AI DECLARATION

Generative artificial intelligence tools, including ChatGPT (OpenAI), were used during manuscript preparation for language refinement, grammar correction, formatting assistance, and organisation of content. All aspects of study design, data collection, statistical analysis, interpretation of results, and scientific conclusions were performed and verified by the authors, who take full responsibility for the accuracy, originality, and integrity of the manuscript.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

ACKNOWLEDGEMENT

The authors gratefully acknowledge the participation of all pharmacy students who took part in this study.

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