Artificial intelligence is transforming the pharmaceutical sector through improvement in critical processes such as quality assurance (QA). However, in Kenya, technical problems in QA processes, including in‐process quality control, equipment maintenance, and visual inspections exist. This paper aims to shed light on the potential of AI in improving pharmaceutical QA in Kenya and challenges associated with its integration. A literature search was thoroughly conducted by retrieving articles from Google Scholar. Articles and policy documents with information relevant to AI applications in QA, optimising pharmaceutical processes, and regulatory compliance in Kenya were reviewed and analysed. AI can improve efficiency and precision in various QA processes including warehousing, equipment maintenance, in‐process quality control, and visual inspections, among others. Significant challenges to AI incorporation in QA of Kenya's pharma companies include a lack of technical expertise and understanding of AI outcomes, high implementation costs and fear of losing jobs. There should be strengthened collaborations among government, pharmaceutical manufacturers, AI companies, and researchers to address skill‐based barriers and financial challenges.

Background: Nonadherence to antipsychotics affects nearly half of patients with schizophrenia, leading to rehospitalization, suicidality, and reduced quality of life. Optimizing treatment adherence is critical to prevent disease relapses, which can be life‐threatening. This review provides an updated status of innovative efforts in ensuring treatment adherence and the challenges associated. It also addresses future directions to improve treatment outcomes among patients with schizophrenia. Methods: A narrative review was thoroughly conducted by retrieving peer‐reviewed articles from PubMed, Google Scholar, ScienceDirect, and Web of Science. Articles relevant to the aim of the review and published in English were reviewed and analyzed. A total of 76 articles were included in the final report. Results: Digital pills have demonstrated adherence detection rates reaching above 95%. Wearable devices such as smart watches collect real‐time behavioral and physiological data such as sleep patterns, heart rate variability, and physical activity that reflect adherence or emerging relapse. These metrics enable clinicians to customize interventions according to the patient adherence profile, allowing shared decision‐making during treatment. Besides, the biomarker‐based monitoring system provides more precise tracking, thus reducing the risk of an unsuitable treatment switch. Aptamer‐based technologies also provide ultrasensitive and noninvasive adherence monitoring through the detection of antipsychotics in body fluids. Conclusion: Integration of wearable and biomarker data enables clinicians to make evidence‐based pharmacological decisions while treating schizophrenic patients. Artificial intelligence promises to facilitate this integration by forecasting medication lapses and suggesting personalized solutions. To fully revolutionize adherence in schizophrenia treatment, pharmacists, psychiatrists, bioengineers, and policymakers should collaborate to address ethical, infrastructural, and financial barriers present in this practice.