Embracing Artificial Intelligence in Malaysian Pharmacy Practice: Current Landscape and Future Directions

Artificial intelligence (AI) is gaining global attention for its potential to transform various aspects of care, including healthcare. In pharmacy, AI is establishing a foothold in clinical decision-making and medication management, and it also holds promise in pharmacovigilance, medication safety, research and development, regulatory processes, and professional education. As countries worldwide advance in these areas, Malaysia’s path toward AI integration in pharmacy deserves closer examination to explore its opportunities and challenges. While the use of AI in pharmacy practice is still in an evolving stage in Malaysia, this article outlines the global application of AI in pharmaceutical care and the potential for Malaysia to adapt these advancements to enhance health screening, identify at-risk populations, optimize healthcare resource allocation, and improve access to healthcare services.

Current Applications of AI in Pharmaceutical Care

AI has been employed internationally to streamline pharmacy operations and improve patient outcomes. A recent scoping review identified three primary applications: (1) identification of atypical or inappropriate medication orders, (2) enhancement of mass screening services, and (3) improvement of medication adherence through the integration of AI in mobile applications. These applications leverage machine learning to analyze big data, supporting pharmacists in making more informed clinical decisions [1]. 

MedAware, for example, uses probabilistic machine learning to detect medication errors by flagging statistically outlier prescriptions, while deep learning models assist pharmacists in identifying blister-pack medications to prevent dispensing errors [2]. Conversational AI systems have also improved medication adherence, as seen in SMS-based refill reminders for Medicare patients [2].

These AI-supported interventions resonate with Geoffrey Rose’s theory of preventive medicine, where both high-risk individual strategies and population-wide measures, such as AI-powered mass screening and medication adherence programs, are necessary to effectively reduce the burden of medication-related morbidity and mortality [3]. Embedding AI integration efforts within a Health in All Policies (HiAP) governance framework could further align with broader national and regional health priorities and cross-sectoral equity considerations.

In addition to supporting clinical pharmacy services, AI is increasingly transforming upstream pharmaceutical processes, including drug discovery and molecular design. Deep learning algorithms, such as generative adversarial networks (GANs) and reinforcement learning models, are now used to generate novel molecular structures with optimized pharmacokinetic profiles. For example, AlphaFold has revolutionized protein structure prediction, allowing researchers to simulate receptor conformations with higher accuracy [4]. Similarly, platforms like DeepDock and AtomNet use deep learning for ligand–protein docking simulations, enabling rapid virtual screening of large compound libraries [5]. These technologies could reduce development timelines and increase the success rate of identifying viable drug candidates, making AI an invaluable tool in pharmaceutical research and development (R&D).

Meanwhile, emerging innovations such as AI-guided nanorobotics present a transformative opportunity for pharmacy practice. Researchers are developing nano-scale robots capable of targeted drug delivery, where AI algorithms are used to calculate optimal dosing, control navigation through biological environments, and regulate the timing of drug release. These systems have shown promise in preclinical models for conditions such as cancer and cardiovascular disease, offering potential improvements in therapeutic precision and minimization of off-target effects [6]. Although still in the early stages of development, these technologies highlight the future trajectory of AI-integrated precision pharmacotherapy.

Challenges

In Southeast Asia, including Malaysia, the adoption of AI in pharmacy is still in its early stages. Factors such as limited infrastructure, lack of standardized electronic health records and varying levels of digital literacy among healthcare professionals are key reasons contributing to the slow uptake. Data privacy concerns, especially regarding patient information, are paramount. Therefore, it is essential to maintain public trust by ensuring compliance with regulations like the Personal Data Protection Act 2010. Additionally, the cost of implementing AI technologies can be prohibitive for smaller community pharmacies, potentially widening the gap between urban and rural healthcare services.

Another significant barrier is the lack of AI-related education and training among pharmacy professionals. A study surveying international pharmacy students revealed that while there was a positive attitude towards AI, many felt inadequately prepared to utilize such technologies in their future careers [7]. This highlights the need for curriculum reforms to incorporate AI competencies, to ensure future pharmacists are equipped with up-to-date AI skills.

Opportunities

Nevertheless, Malaysia stands at a strategic position to leverage AI in pharmacy practice, supported by its robust healthcare infrastructure and a growing pool of tech-savvy professionals. Collaborations between academic institutions, healthcare providers, pharmaceutical companies, and tech firms are fostering the development of AI tools tailored to local needs. Some pharmaceutical companies are developing advanced AI platforms, such as those used for cancer screening, which could pave the way for impactful public-private partnerships. In addition, AI-driven solutions can help address non-communicable diseases prevalent in Malaysia, such as diabetes and cardiovascular conditions, through personalized medication counselling, adherence checkers with reminders, and continuous patient monitoring.

Moreover, AI-driven predictive analytics has the potential to reshape population health by identifying individuals at risk of chronic disease or readmission, enabling earlier and more targeted clinical interventions. These predictive tools align with population health management approaches by stratifying populations based on risk profiles, allowing health systems to prioritize patient outreach, tailor treatment strategies, and allocate resources more efficiently [8]. ​

While AI offers diverse opportunities, it is crucial to consider Malaysia’s unique healthcare landscape. Rural and underserved regions continue to face disparities in access to pharmaceutical services due to workforce shortages and infrastructure gaps. In this context, AI can serve as a powerful enabler of equitable care. For example, AI-powered telepharmacy platforms could facilitate remote medication reviews and pharmaceutical consultations, while chatbot-based tools may help improve health literacy and medication adherence. Additionally, predictive analytics could assist the Ministry of Health in anticipating medicine demand trends and optimising national supply chain distribution systems. These applications align with national digital transformation efforts outlined in the Malaysia Digital Economy Blueprint (MyDIGITAL), which envisions an inclusive, digitally empowered healthcare ecosystem. By integrating pharmacy-specific AI innovations into this broader agenda, Malaysia can bridge the urban–rural divide and modernise pharmaceutical care delivery in both public and private sectors [9].

Future Directions

To fully realize the benefits of AI in pharmacy practice, a comprehensive and multi-faceted approach is required (Figure Ⅰ). First, there must be deliberate efforts in policy development. While AI continues to advance rapidly, it is worth noting that currently, there is no comprehensive, up-to-date legislation in place to regulate its use in healthcare. Establishing clear guidelines and regulatory frameworks to govern the use of AI in healthcare is essential. These frameworks should prioritize ethical considerations, data privacy, accountability, and patient safety, ensuring that AI tools are implemented responsibly and in ways that enhance the standard of care. Transparent policies empower practitioners and institutions to adopt AI-driven solutions with greater confidence by reducing legal ambiguity.

Equally important is the integration of AI-related education and training into pharmacy curricula. Future pharmacists must be equipped not only with clinical and pharmacological knowledge but also with digital literacy and a foundational understanding of AI technologies. Pharmacy programs should embed modules that teach students about the applications, limitations, and ethical use of AI. Additionally, continuous professional development (CPD) initiatives should be introduced to upskill current practitioners, enabling the existing workforce to adapt to technological advancements and engage with AI systems responsibly in daily practice.

Research and development efforts must also be actively encouraged to explore the efficacy, safety, and outcomes associated with AI applications in pharmacy. To ensure AI innovations are evidence-based and practically beneficial to both patients and practitioners, efforts should focus on supporting pilot projects, clinical trials, and interdisciplinary collaborations among pharmacists, data scientists, and healthcare providers. Future evaluations could adopt established public health implementation frameworks, such as RE-AIM, to assess AI interventions, ensuring they achieve meaningful patient outcomes.

Implementing AI in pharmacy practice often involves significant upfront investments in infrastructure, training, and system integration, making cost-effectiveness analysis essential. These evaluations would benefit from established Health Technology Assessment (HTA) principles to ensure the cost-effectiveness of AI-driven pharmacy solutions in resource-constrained environments. AI itself can support these evaluations by automating data extraction from electronic pharmacy records, stratifying patients by clinical risk, and running predictive models to estimate cost and outcome impacts.  

Conclusion

The integration of AI into Malaysian pharmacy practice offers transformative potential for individual patient care and broader public health outcomes. By improving medication safety, expanding access to rural communities, and strengthening pharmacovigilance, AI can play a pivotal role in supporting Malaysia’s national healthcare goals. However, equitable implementation, robust data governance, and interdisciplinary collaboration among policymakers, pharmacists, and public health experts are essential to maximize the benefits of implementing AI in pharmacy practice. Strategic investments in AI today can pave the way for a more efficient, equitable, and resilient pharmaceutical care system in Malaysia.

CONFLICT OF INTEREST

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors declare no conflict of interests.

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