Co-written by Dr KONE YEREGUENI ABIBA, pharmacist, and BONIFACE NWEMENI, IT project manager and digital transformation specialist
Abstract
This article examines how artificial intelligence (AI) is gradually transforming the pharmaceutical sector and the opportunities it may create for African countries. From prescription analysis and pharmacovigilance to stock management, combating falsified medicines and industrial production, AI can help professionals make better use of available information, provided it is properly validated and supervised.
For Africa, the challenge is twofold. Health systems across the continent face medicine supply difficulties, the circulation of substandard or falsified medical products, and unequal access to healthcare. AI can help address some of these problems, but it cannot replace pharmacists or investment in institutions, data and skills.
The article examines the contribution of AI to medicine safety, pharmacovigilance, personalised treatments, stockout prevention, action against falsified medicines and pharmaceutical production. It then considers existing African initiatives, professional opportunities, risks and the conditions for responsible adoption.
Key points at a glance
The opportunity: Identify possible drug interactions and high-risk prescriptions, forecast medicine requirements more accurately to reduce stockouts, and help sort pharmacovigilance reports.
The obstacles: Fragmented information systems, a shortage of reliable local data, and the under-representation of African populations in research datasets.
The principle: AI supports professional practice; verification and decisions remain in human hands.
1. Why is artificial intelligence transforming pharmacy?
Pharmacy involves analysing large amounts of sometimes complex information, which AI can help process more quickly. When reviewing a prescription, a pharmacist must consider the patient’s age, medical history, allergies, current treatments, possible interactions and prescribed doses. AI can flag situations that require closer examination.
Machine learning makes it possible to develop systems that identify patterns in large volumes of data. Other technologies can facilitate the analysis of scientific literature, clinical reports and suspected adverse drug reaction reports. The European Medicines Agency (EMA) recognises potential AI applications throughout the medicine lifecycle, from research to post-marketing surveillance (EMA, 2024).
However, AI should be distinguished from conventional software. A program that records sales or generates an alert when stock reaches a predetermined threshold does not necessarily use AI. An AI-based system, by contrast, may combine multiple variables to predict future requirements. The aim is not to introduce AI everywhere, but to choose the most appropriate tool for the problem.
2. How can AI improve medicine safety?
AI can help pharmacists identify potentially unsafe prescriptions, but verification and decisions remain the responsibility of the professional. A prescription may contain an inappropriate dose, a contraindication, a drug interaction, therapeutic duplication or a treatment requiring particular monitoring. Some decision-support systems draw attention to these situations; the pharmacist must then check the alert, interpret it in the patient’s circumstances and decide what action to take.
In practice, an alert follows four stages:
1. Data: The tool analyses the prescription and available patient information.
2. Alert: The system flags a possible interaction, unusual dose or contraindication.
3. Verification: The pharmacist assesses the relevance of the alert in the context of the patient.
4. Decision: The pharmacist approves, adjusts or declines to dispense the medicine, consulting the prescriber when necessary.
A scoping review by Hatzimanolis and colleagues (2025) found that one of the most common applications of AI in pharmacy was identifying prescriptions requiring a pharmacist’s intervention. The authors also observed that the applications reviewed focused more on workflow efficiency than on directly improving patients’ health outcomes.
Another review, published in 2026 by Al-Taie, Abdullah and Arueyingho, confirms growing interest in these technologies for medication safety and prescription analysis in clinical pharmacy services.
These findings emphasise the distinction between a system’s technical performance and demonstrated clinical benefit. Software that detects a possible interaction does not automatically improve a patient’s health: the alert must be relevant, the professional must respond appropriately, and care must improve as a result.
3. Can artificial intelligence strengthen pharmacovigilance?
Yes, but only when the underlying data are reliable. Pharmacovigilance involves detecting, assessing, understanding and preventing adverse effects of medicines. Reports come from patients, health professionals, hospitals, pharmaceutical manufacturers and regulators; processing them can require substantial resources when their volume increases.
AI can help classify reports, analyse written accounts and identify unusual patterns. A signal detected by a computer system does not, however, prove that a medicine caused an adverse event. Scientific assessment remains essential.
For African countries, the first question concerns data quality. When adverse effects are under-reported, information is scattered or systems cannot communicate, AI performance is limited. Stronger reporting and data collection mechanisms should therefore precede investment in advanced analytical tools.
4. Towards more personalised pharmacy: what opportunities exist for Africa?
AI could help tailor treatment to individual patients, provided the tools are validated in African populations. Age, health status, kidney and liver function, other treatments and certain genetic characteristics influence how a person responds to a medicine. Researchers are investigating systems that analyse clinical and biological information to help professionals anticipate therapeutic responses.
A model developed mainly from data about particular populations may become less reliable in other settings. This limitation is particularly relevant to Africa: its populations have considerable genetic diversity, yet remain under-represented in international genomic studies and large research databases.
The Assessing Genetic Diversity in Africa (AGenDA) project, described in Nature by Ramsay and colleagues (2026), seeks to help address this imbalance. Led by African researchers, it brings together institutions from several countries, including Rwanda, Kenya, the Democratic Republic of the Congo, Tunisia, Angola and Zimbabwe.
African researchers must therefore be involved in responsible data collection, tool development and validation. More personalised pharmacy also requires laboratories, diagnostic capacity, specialist expertise and robust protection of personal information.
5. How can AI help prevent medicine stockouts?
AI can improve demand forecasting, but it cannot replace a functioning supply chain. A stockout may interrupt treatment, force patients to travel long distances or increase their costs. Conversely, excessive stock ties up financial resources and creates losses when products expire.
By analysing historical consumption, seasonal changes and delivery times, a forecasting system could, for example, alert a pharmacy to an expected rise in demand for a medicine and help it place orders earlier. This approach may benefit community pharmacies, hospitals, pharmaceutical wholesalers and central purchasing agencies.
Forecasting is not enough when suppliers have no products, funding is unavailable or logistics are disrupted. A forecast is useful only if the establishment has the money to order: in many African pharmacies, cash purchases and delays in reimbursement from insurers or mutual health schemes limit the ability to plan ahead. Forecasting tools should therefore account for cash-flow cycles and working capital; otherwise, an anticipated shortage may still be unavoidable. Each establishment should also assess whether AI offers a meaningful improvement over conventional inventory management software.
6. Substandard and falsified medicines: what role can digital technology and AI play?
Digital technologies already help combat falsified medicines; AI could complement these measures without replacing regulatory inspections. According to an estimate published by the World Health Organization in 2017 and repeated in its 2024 fact sheet, at least one in ten medical products in low- and middle-income countries is substandard or falsified (WHO, 2017; WHO, 2024b). This estimate covers those countries collectively and is not an Africa-specific figure.
A substandard medicine does not meet the applicable quality standards or specifications. A falsified medicine deliberately misrepresents its identity, composition or source. Such products can cause treatment failure, adverse effects, unnecessary expense and, in some cases, severe harm.
Nigeria: a practical example of digital medicine authentication
In Nigeria, the National Agency for Food and Drug Administration and Control (NAFDAC) introduced the Mobile Authentication Service (MAS) in 2010. Consumers scratch a label on the package to reveal a single-use code, then send it by free text message to check whether the product is authentic. Official guidelines govern the scheme (NAFDAC, 2018).
This is a digital authentication system, not an AI application. AI could nevertheless complement it by analysing product data, spotting anomalies in distribution channels or flagging cases requiring inspection. It would not replace inspections, laboratory testing or regulatory action. Combating falsified medicines requires technology alongside regulation, supply-chain oversight and patient education.
7. Can AI support African pharmaceutical manufacturing?
AI can support medicine production, but analysis of its potential in Africa is still developing. In a pharmaceutical factory, digital systems and AI can help analyse production data, detect anomalies, optimise processes, support quality control and anticipate equipment maintenance. These benefits must nevertheless be demonstrated under real production conditions.
In February 2026, WHO issued a call for proposals to analyse how digitalisation could strengthen local pharmaceutical production and cooperation with the private sector in Africa (WHO, 2026a). On 21 May 2026, alongside the 79th World Health Assembly, an event in Geneva organised by the Global Initiative on Digital Health, Germany’s Federal Ministry for Economic Cooperation and Development (BMZ) and the European Commission examined digital transformation in African pharmaceutical manufacturing (WHO, 2026b; BMZ, 2026).
These activities demonstrate growing institutional interest but do not establish that AI has already improved the performance of African manufacturers. Local production also depends on industrial investment, skilled personnel, quality-control systems, effective regulation and reliable access to raw materials. AI must therefore form part of a broader industrial and public-health strategy.
8. Which African initiatives can support this transformation?
Africa is not starting from scratch. Several continental and national institutions are already laying the groundwork for responsible adoption.
The African Medicines Agency
The African Medicines Agency (AMA), a specialised agency of the African Union, aims to strengthen and harmonise regulation of medical products across the continent. In May 2026, WHO and AMA signed a cooperation framework intended to strengthen regulatory systems, product safety monitoring and access to quality-assured medical products (WHO, 2026c). This agreement may support a more enabling environment for innovation, but it is not itself an AI deployment programme for pharmacies.
The African Union’s Continental AI Strategy
In July 2024, the African Union adopted a continental strategy on artificial intelligence. It recognises AI’s potential in areas including health, while emphasising responsible, inclusive use aligned with African priorities (African Union, 2024). For pharmacy, it could encourage local research, professional training and regional cooperation.
National medicines regulators and pharmacovigilance authorities
National regulators remain responsible for safeguarding medicine quality and safety. Nigeria’s authentication scheme illustrates how a regulator can introduce technology suited to a practical problem. These institutions need to be strengthened before complex tools are introduced.
9. What opportunities does AI offer African pharmacists, researchers and entrepreneurs?
AI offers professional and economic opportunities, provided it addresses genuine needs.
For pharmacists
Pharmacists do not need to become software developers, but they should understand AI well enough to assess its benefits and limitations. Training should cover how to verify AI-generated information, protect patient data and interpret results.
For researchers and universities
African universities can bring pharmacy, computer science, public health and data science closer together. Research teams can investigate medicine demand forecasting, pharmacovigilance and the performance of decision-support tools in local settings. They can also develop joint training opportunities for pharmacy and computing students.
For engineers and entrepreneurs
Possible innovations include specialist information assistants linked to verifiable scientific sources, stock forecasting systems, tools for analysing adverse-event reports and integrated pharmacy management software. These are potential business concepts, not a list of existing companies with verified results.
Before launching a project, entrepreneurs should establish users’ needs, assess data quality, understand regulatory obligations and calculate development, operating and maintenance costs. Useful innovation must be accessible, reliable and economically sustainable.
10. What are the main risks of AI in pharmacy?
The main risks concern errors, bias, data confidentiality, technological dependence and unclear accountability.
Errors and misleading information
Generative AI can produce plausible but incorrect answers. Inaccurate information about doses, contraindications or drug interactions can have serious consequences. Results must therefore be checked against recognised scientific sources.
Bias and inequality
A model trained on insufficient data from particular populations may be less reliable for those groups. Tools intended for African patients should be evaluated in the settings where they will actually be used.
Data privacy
Health data are especially sensitive. Information identifying a patient should not be shared with an external AI service without appropriate authorisation and legal and technical safeguards.
Technological dependence
An establishment that cannot control access to its data, maintenance costs or its ability to change software providers becomes dependent on its supplier. Suitable contracts and local expertise can help limit this risk.
Professional responsibility
When a tool produces an incorrect recommendation, questions of responsibility arise. These should be addressed before deployment, taking account of the application, professional roles and applicable law. WHO emphasises governance, risk assessment, rights protection and the participation of professionals and patients in developing AI for health (WHO, 2024a).
11. How can AI be adopted responsibly?
Responsible adoption should be gradual and proportionate to available resources. African countries do not need perfect infrastructure before exploring practical applications. Five steps can guide establishments.
Step 1: Identify priority problems
Medicine stockouts, administrative workload, adverse-event monitoring or access to drug information: each establishment should first define the problem it needs to solve.
Step 2: Improve data quality
AI systems depend on the information they receive. Collecting, checking and managing data should therefore be a priority.
Step 3: Train professionals
Pharmacists, doctors, IT specialists and managers need to understand the possibilities, limitations and risks of the tools they use.
Step 4: Test and evaluate solutions
An application should not be adopted simply because it uses AI. Its results, costs and effects on professional practice must be measured, with stricter requirements for tools influencing treatment decisions.
Step 5: Establish oversight
Responsibilities, confidentiality rules, verification procedures and system monitoring must be defined.
Ten joint principles published by EMA and the US Food and Drug Administration (FDA) on 14 January 2026 provide useful guidance: human-centred design, a risk-based approach, data governance, multidisciplinary expertise and management across the technology lifecycle (EMA and FDA, 2026). Their application must nevertheless reflect national regulatory requirements.
12. Why will local expertise and collaboration be decisive?
The future of pharmaceutical AI in Africa will depend less on where technologies originate than on African institutions’ ability to understand and control them. Tools developed elsewhere may be useful, but populations, languages, healthcare practices and available medicines differ between countries. Options include building local solutions, adapting proven tools, forming scientific partnerships or negotiating contracts that protect control over data and maintenance. In every case, digital sovereignty requires local skills, transparent contracts and independent evaluation capacity.
No single profession has all the necessary expertise. Pharmacists understand medicines and patients’ needs; IT specialists design and integrate systems; data scientists develop and evaluate models; and regulators set safety and quality requirements. African universities, hospitals, technology companies and manufacturers would benefit from joint projects.
Governments have a vital role in financing research, digital infrastructure and regulatory frameworks. Investors should prioritise solutions that meet demonstrated needs rather than products marketed primarily on their use of AI. Cooperation between African countries can also accelerate knowledge-sharing and the development of shared capabilities.
Conclusion: innovation must serve patients
Artificial intelligence cannot solve the challenges facing African pharmaceutical systems on its own. It cannot replace healthcare professionals, investment in medicine supplies, or regulatory and quality-control mechanisms. It can, however, make a useful contribution to prescription analysis, pharmacovigilance, inventory management, combating falsified medicines and industrial production.
To benefit, African countries should begin with clearly identified needs, rely on dependable data, rigorously evaluate tools and invest in local skills rather than remaining exclusively users of technologies designed and controlled elsewhere. This transformation requires collaboration among pharmacists, doctors, researchers, engineers, entrepreneurs, manufacturers, health authorities and policymakers.
The pharmacy of the future will not necessarily be the one in which machines perform the greatest number of tasks. It will be the one in which appropriate technologies help professionals provide better care. The aim is not to replace people, but to strengthen their ability to serve patients.
Frequently asked questions
Can artificial intelligence replace a pharmacist?
No. AI can analyse certain information and automate tasks, but it cannot replace professional judgement, clinical responsibility or the relationship with the patient.
How can AI reduce medicine stockouts in Africa?
By forecasting demand using past consumption, seasonal variation and supply lead times. Its effectiveness still depends on reliable data and functioning distribution systems.
Can artificial intelligence detect falsified medicines?
It can contribute by complementing digital authentication and traceability tools and identifying unusual patterns in distribution networks. It cannot replace regulatory inspections or laboratory testing.
Do African pharmacists need training in artificial intelligence?
Yes. Appropriate training helps them understand the possibilities, limitations and risks of AI, particularly when verifying information, protecting data and safeguarding patients.
What opportunities does AI offer African entrepreneurs?
Key opportunities include pharmacy management, stock forecasting, specialist information search and pharmacovigilance support. Solutions must address specific needs and comply with professional and regulatory requirements.
What are the main barriers to adopting AI in African pharmacies?
A shortage of reliable data, cost, inadequate digital infrastructure, training requirements, regulatory challenges and difficulties with maintenance.
References
Al-Taie, A., Abdullah, A.S. et Arueyingho, O. (2026) ‘Integration of artificial intelligence applications in clinical pharmacy services: A scoping review’, Future Healthcare Journal, 13(3), article 100547. Available at: https://doi.org/10.1016/j.fhj.2026.100547
Bundesministerium für wirtschaftliche Zusammenarbeit und Entwicklung (BMZ) (2026) BMZ Side Events at the 79th World Health Assembly. Healthy DEvelopments. Available at: https://health.bmz.de/?p=22661
European Medicines Agency (EMA) (2024) Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle. EMA/CHMP/CVMP/83833/2023. Available at: https://www.ema.europa.eu/en/scientific-guidelines/use-artificial-intelligence-ai-medicinal-product-lifecycle
European Medicines Agency (EMA) et Food and Drug Administration (FDA) (2026) Guiding Principles of Good AI Practice in Drug Development. 14 janvier. Available at: https://www.fda.gov/about-fda/artificial-intelligence-drug-development/guiding-principles-good-ai-practice-drug-development
Hatzimanolis, J., Riley, B., El-Den, S., Aslani, P., Zhou, J. et Chaar, B.B. (2025) ‘Applications of artificial intelligence in current pharmacy practice: A scoping review’, Research in Social and Administrative Pharmacy, 21(3), pp. 134–141. Available at: https://doi.org/10.1016/j.sapharm.2024.12.007
National Agency for Food and Drug Administration and Control (NAFDAC) (2018) Guidelines for Procurement and the Management of the Mobile Authentication Service (MAS) Scheme in Nigeria. Abuja : NAFDAC. Available at: https://www.nafdac.gov.ng/wp-content/uploads/Files/Resources/Guidelines/PVG_GUIDELINES/NAFDAC-MAS-Guidelines-Recent.pdf
World Health Organization (WHO) (2017) 1 in 10 medical products in developing countries is substandard or falsified. 28 novembre. Disponible sur : https://www.who.int/news/item/28-11-2017-1-in-10-medical-products-in-developing-countries-is-substandard-or-falsified
World Health Organization (WHO) (2024a) Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. Genève : OMS. Disponible sur : https://www.who.int/publications/i/item/9789240084759
World Health Organization (WHO) (2024b) Substandard and falsified medical products. Fiche d’information. Disponible sur : https://www.who.int/news-room/fact-sheets/detail/substandard-and-falsified-medical-products
World Health Organization (WHO) (2026a) Request for proposal: Situation Analysis on the Intersection of Digitalization, Pharmaceutical Production, and Private Sector Cooperation in the African Region. 19 février. Disponible sur : https://www.who.int/news-room/articles-detail/request-for-proposal-situation-analysis-on-the-intersection-of-digitalization-pharmaceutical-production-and-private-sector-cooperation-in-the-african-region
World Health Organization (WHO) (2026b) Pharma 4.0: Strengthening local production of pharmaceuticals and the supply chain systems in Africa through digitalization. 21 mai. Disponible sur : https://www.who.int/news-room/events/detail/2026/05/21/default-calendar/pharma-4.0--strengthening-local-production-of-pharmaceuticals-and-the-supply-chain-systems-in-africa-through-digitalization
World Health Organization (WHO) (2026c) WHO and African Medicines Agency launch landmark framework to strengthen access to quality-assured health products in Africa. Mai. Disponible sur : https://www.afro.who.int/news/who-and-african-medicines-agency-launch-landmark-framework-strengthen-access-quality-assured
Ramsay, M. et al. (2026) ‘Enriching African genome representation through the AGenDA project’, Nature, 649, pp. 565–573. Available at: https://doi.org/10.1038/s41586-025-09935-7
African Union (2024) Continental Artificial Intelligence Strategy: Harnessing AI for Africa’s Development and Prosperity. Addis-Abeba : Commission de l’Union africaine. Disponible sur : https://au.int/en/documents/20240809/continental-artificial-intelligence-strategy
About the authors
Dr KONE YEREGUENI ABIBA
Pharmacist and Doctor of Pharmacy. Her expertise brings a scientific and professional perspective on medicines, pharmacy practice and patient care.
BONIFACE NWEMENI
IT project manager specialising in digital transformation, artificial intelligence and business data analytics. His expertise covers information systems, IT project management and the integration of emerging technologies into organisations.
A joint perspective from two disciplines
This article brings together two complementary fields: pharmaceutical sciences and information technology. Its central conviction is that digital pharmacy cannot be developed by IT specialists or healthcare professionals working alone, but through collaboration to create reliable, responsible solutions adapted to the needs of African populations.
Disclaimer
This article is intended for general information, professional reflection and discussion of the potential uses of artificial intelligence in pharmacy. It is not individual medical advice, a treatment recommendation or clinical validation of any technology described. AI tools that may influence patient care must meet applicable scientific, professional, ethical and regulatory requirements.
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