Let us be frank: if we are to have any hope of an AI healthcare system that is both efficient and worthy of our trust

The gap

Evidence profile

Sourced from the future work and recommendations of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Next-Generation Pharmacovigilance: The Role of AI and Machine Learning in Detecting and Managing Drug Risks (2026) · Interdisciplinary Journal of the African Alliance for Research Advocacy and Innovation · doi

  How to cite this article: Arshad et al. (2026). Next-Generation Pharmacovigilance: The Role of AI and Machine Learning in Detecting and Managing Drug Risks. Interdisciplinary Journal of the African Alliance for Research, Advocacy and Innovation. Vol 2, Issue 2. April-June. https://doi.org/10.64261/x8f2hr74. Interdisciplinary Journal of the African Alliance For Research, Advocacy & Innovation ISSN (O) : 3093-4664 With the further development of data ecosystems, analytics, and regulatory standards, the future of AI and ML in pharmacovigilance is going to become more advanced. Further studies ought to be carried out to come up with explainable and transparent AI models to increase trust, interpretability, and regulatory acceptance. Interpretable deep learning and causal inference are the methods that may help to overcome the divide between algorithmic predictions and clinical decision-making. The other important direction is the development of high quality and standardized as well as real world data that can be used in the training of the model on a variety of population. Electronic health records, global safety databases, and social media platforms will be interoperable, which will facilitate a more thorough safety surveillance. It requires shared information systems between regulatory agencies, pharmaceutical firms, and health care systems to facilitate this. Multimodal AI-based methods that combine clinical text, genomics, wearable device data, and imaging to support individual risk prediction of ADRs will also be the future of pharmacovigilance. Moreover, AI systems with the ability to detect signals in real-time and automatically screen cases can considerably decrease the delays in reporting and the load of work. The regulatory agencies need to keep on streamlining rules regarding validation, auditing, and lifecycle monitoring of AI systems. Safe adoption will be reliant on the introduction of human-AI collaboration models, like those in which AI assists, but not rules out the expertise. Ethical aspects, such as data privacy, reduction of bias, and accountability, will be the primary points of focus. In general, the future environment will cease to be reactive in terms of ADR reporting, that is, become proactive and predictive (as well as patient-centered) in terms of drug safety through the power of robust, transparent, and ethically based AI technologies.

  generalfuture work
  Keywords: regulatory systems pharmacovigilance future safety learning drug interdisciplinary journal african alliance advocacy innovation further development
• GENERATIVE ARTIFICIAL INTELLIGENCE AND LARGE LANGUAGE MODEL IN PHARMACOVIGILANCE (2026) · European Journal Pharmaceutical and Medical Research · doi

  www.ejpmr.com │ Vol 13, Issue 5, 2026. │ ISO 9001:2015 Certified Journal │ 171 Vishwakarma et al. European Journal of Pharmaceutical and Medical Research to street and lives from home language processing revolution in pharmacovigilance has been brought about by AI-driven automation, which uses machine learning models, natural (NLP), and sophisticated algorithms to quickly and effectively evaluate massive amounts of real-world data sources.[4] The early 20th century to the evolving era (21st century), public healthcare sectors have exponentially developed, evolved not only in terms of concept, and application but with the progression of time it has advanced its technologies in the scientific field. Healthcare sectors are responsible for the production and storage of large amounts of data and the management of these became a major challenge for nations across the world, hence the need for advancement in technologies like big data and Artificial intelligence arose.[5] A wide range of fields and new intricate angles for the usage of big data are being discovered by the thoughtful brains of the Scientific community.[6] More advanced AI approaches such as large language models (LLMs) have expanded what is possible for extraction and reasoning from clinical text, but they are fallible in high-stakes settings without safeguards.[7] Large Language Models (LLMs) can produce factually incorrect or unfaithful statements (―hallucinations‖) and omissions, reinforcing the need for designs that foreground provenance and constrain outputs.[8] Artificial intelligence (AI) is the dawn of a new era Unknowingly, it has become an integral part of our personal the technology is now pervading scientific research, health- care system, and pharmacovigilance (PV) PV is to reduce the incidence and the risk associated with the use of medicines.[9] Machine learning (ML) and artificial intelligence (AI) have a long history of use in health care, including scheduling, radiology imaging analysis, drug discovery, and clinical diagnostic and decision support, with variable rates of success.[10] Machine learning (ML) and artificial intelligence (AI) have a long history of use in health care, including scheduling, radiology imaging analysis, drug discovery, and clinical diagnostic and decision support, with variable rates of success.[11] Large language models (LLMs) are useful tools with the capacity for performing specific types of knowledge work at an effective scale. However, LLM deployment

  generalrecommendations
  Keywords: language models large artificial intelligence machine learning scientific llms clinical health care journal pharmacovigilance amounts
• Artificial Intelligence-Based Healthcare Systems: A Review of Machine Learning, Deep Learning, Data Analytics, Supply Chain Management, and Electrical Engineering Technologies (2026) · Global Trends in Science and Technology · doi

  Let us be frank: if we are to have any hope of an AI healthcare system that is both efficient and worthy of our trust, we have to square up with the difficult issues of data quality, privacy, ethics and interpretability. You can’t have one without the other; it is a condition for any solution you want to see gain traction in the long run. The path ahead for AI in this sector is going to be transformative in the truest sense [60]. We are facing a very dynamic future driven by advances in computing, data science and biomedical engineering. Once we leave the present deficiencies in our wake and let new innovations take root, tomorrow’s systems will be more predictive and decentralized, with a better feel for the individual patient. We want to do more than just improve the accuracy of a diagnosis or treatment, we want to make healthcare as a whole more accessible and on the right side of ethics [61]. Consider XAI, or explainable artificial intelligence. It is perhaps the most important thing on the horizon. An AI should not be making decisions that impact human lives in a black box. There has to be transparency. A doctor has to be able to follow the logic of a recommendation before he puts his faith in it. XAI gives you that clarity and keeps accountability in the clinic. Or look at federated learning, which is essential for protecting patient privacy [62]. The old way of centralizing data for

  generalfuture work
  Keywords: want healthcare privacy ethics patient frank hope system efficient worthy trust square difficult issues quality

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