Joshua Mantey: Use of AI in drug delivery

Ghana has expanded access to medicines in recent years, but the daily obstacles to patient prices remain sluggish and the supply lines may be leaking, not all doses reach people when needed. Research into medical reform in Ghana shows progress along with sustained gaps in pricing, funding and supply chains, particularly outside major cities. [1, 2, 3].

There is also a quiet threat. It is when a low-quality or fake drug slips into the system. For many years, the World Health Organization has warned that substandard drugs often remain a serious risk for antibiotics and antimalarial drugs across low and middle-income environments, including parts of Africa. [4, 5, 6].

Amidst these pressures, a new set of tools has shifted from lab talk to practical help: Artificial Intelligence (AI) for Drug Delivery. This is not just about inventing new drugs. The evidence shows that AI can support three highly earthly goals. It creates a dosage shape that works well in real life, keeps quality and supply on track, and helps people stick to treatment.

First, AI helps in the design of tablets and capsules that release side effects and waste at an appropriate rate within the body, while improving effectiveness. A recent review explains how machine learning allows you to choose ingredients, particle sizes, and release profiles to make your medication easier to take and more reliable in everyday conditions. [7, 8, 9].

The related area is 3D printing of “personalized” pills that combine “polypills” for people currently working with multiple medications per day. Although not all treatments, the literature points to steadily advances towards simpler, patient-friendly administration, which can approach the point of care [10, 11].

Second, AI can enhance quality and supply. The same pattern spotting method that flags financial fraud can flag abnormalities in medical orders, shipping records, or reported side effects. When combined with barcodes and serialization, these tools help regulators and large buyers to catch suspected batches that will early match a “prevention, detection, and response” approach to substandard products. [4, 5].

Third, AI can make compliance support smarter without expensive gadgets. In northern Ghana, randomized studies found that simple text reminders can help people complete anti-malarial treatments. [12, 13, 14]. AI can build on its low-cost basis by adjusting message timing and people at the highest risk of stopping content early, and connecting alerts to community health workers when additional support is needed.

What does this really look like for Ghana's top health Baden:
•Malaria: Thermal-stable, affordable formulation features text messaging support tailored to the local context. Measure important things in the clinic: symptoms, side effects, and treatment completion [10, 12, 13, 14].
HIV: Simpler dosing of the same playbook as possible, target reminders for those at risk of missing dosages, and adapting fast feedback loops to care teams [7, 8, 9].
Chronic Diseases and Cancer Care: Use AI-stimulated formulations and, where feasible, 3D printed personalized doses to reduce tablet burden and improve treatment consistency [10, 11].

The practical playbooks that emerge from the literature look like this. Build a “data pipe” so that the tools can learn from everyday care. Serialization and anomaly detection will tighten quality control across the supply chain. And to work for the coalition, universities, health services, regulators and local manufacturers move in the same direction, keeping innovation affordable under national insurance [1, 2, 3, 4, 5, 7, 8, 10].

The bottom line of the study is simple. Smarter delivery makes existing medicines more effective. AI helps in designing doses that are tailored to daily life, helping people continue to treat, and strengthening guardrails to protect bad products from the system. Coupled with Ghana's continued reforms, this is a realistic path to more effective, safer and more affordable care, from the largest urban hospitals to the most remote clinics.

reference

Koduah A, et al. “Implementing drug pricing policies in Ghana.” International Journal of Health Policy and Management. 2023. https://pubmed.ncbi.nlm.nih.gov/38618785/
Koduah A, et al. “How and why are drug reforms contributing to Ghana's universal health insurance?” Public health frontier. 2023. https://www.frontiersin.org/articles/10.3389/fpubh.2023.1163342/full
Koduah A, et al. “Implementation of drug pricing policies in sub-Saharan Africa: a systematic review.” Systematic review. 2022. https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/S13643-022-02114-Z
World Health Organization. “Substandard and counterfeited medical products” (factsheet). Updated in 2024. https://www.who.int/news-room/fact-sheets/detail/substandard-and-falsified-medical-products
World Health Organization. “One in 10 people in developing countries medical products are substandard or counterfeited.” 2017. https://www.who.int/news/item/28-11-11-17-1-in-10-medical-products-in-developing-countries-is-substandard-or-falsified
Mekonnen BA, et al., “Preservation of substandard, counterfeit, unlicensed, unregistered drugs in Africa: a systematic review.” 2024. https://pmc.ncbi.nlm.nih.gov/articles/pmc11251437/
Vora LK, et al. “Pharmaceutical Technology and Artificial Intelligence in Drug Delivery.” Pharmaceutical Sciences. 2023. https://pmc.ncbi.nlm.nih.gov/articles/pmc10385763/
Gholap Ad, et al. “Advances in Artificial Intelligence for Drug Delivery and Development.” Computers in Biology and Medicine. 2024. https://www.sciencedirect.com/science/article/pii/s001048252400787x
Serrano Dr, et al. “Artificial Intelligence (AI) Applications in Drug Discovery and Development.” Pharmacy. 2024. https://pmc.ncbi.nlm.nih.gov/articles/pmc11510778/
Yasin H, et al. “Production of pharmaceutical dosage forms of polypills by fusion deposition modeling 3D printing.” 2024. https://pmc.ncbi.nlm.nih.gov/articles/pmc11510916/
Kapoor DU, et al. “Innovative Applications of 3D Printing in Personalized Drug Delivery.” Iscience. 2025. https://www.cell.com/iscience/fulltext/S2589-0042(25)01766-3
Raifman JRG, et al., “The impact of text message reminders on compliance with antimalarial treatments in northern Ghana: a randomized trial.” PLOS 1. 2014. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0109032
ClinicalTrials.gov. NCT01722734. “Text reminders to enhance compliance with behaviour in northern Ghana.” https://clinicaltrials.gov/study/NCT01722734
Harvard Dataverse. “The impact of text message reminders on compliance with antimalarial treatments in northern Ghana: a randomized trial” (dataset). https://dataverse.harvard.edu/dataset.xhtml?persistentid=doi:10.7910/dvn/m4ly6c

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