Predictive Pharmacovigilance AI-driven models

The gap

Evidence profile

Sourced from the future work and inline gaps and recommendations of the source papers, classified as general, spanning 6 journals.

Supporting evidence — 7 representative gaps

• ARTIFICIAL INTELLIGENCE IN DRUG RESPONSE PREDICTION AND PHARMACOLOGICAL THERAPY OPTIMIZATION: ADVANCES, CHALLENGES, AND FUTURE PERSPECTIVES (2026) · European Journal Pharmaceutical and Medical Research · doi

  Explainable AI (XAI): Creating AI models that are interpretable will be crucial to improving the confidence of clinicians and their regulatory approval. Approaches like attention mechanisms, feature importance mapping, and surrogate modelling are designed to improve the understandability and the usability of the outputs of this artificial intelligence. In order to explain the reasoning the AI, there are various behind the decision in techniques such as attention mechanism, feature importance mapping, or surrogate modelling that are used the decision-making process more understandable and actionable. to make Multi-Omics Integration: The future will see the integration of multiple layers of omics data, such as genomics, epigenomics, transcriptomics, proteomics and metabolomics, to fully propound the complexity of biological systems, which needs powerful artificial intelligence systems. This integrative approach holds great potential in terms of better predictions of response to treatment and better understanding of the mechanisms of drug action and resistance. Real-World Data Utilization: Using data from electronic health records (EHRs), wearable devices, mobile health apps, and patient-reported outcomes will help capture real-world patient heterogeneity and variability, which can be incorporated into AI models. In the clinic, this integration can help in the continuous improvement of the model and fine-tuning of therapy, enabling improved treatment for each patient. Adaptive Learning Systems: AI systems that have the ability to adaptively learn will adjust and upgrade their forecasts in real-time when new information is added. This ongoing learning approach keeps AI tools up to date with constantly evolving medical information, patients, and populations, resulting in sustained effectiveness. Cross-Disciplinary CPs: Creation of multi-disciplinary collaborations between the fields of AI research, clinical, pharmacological, regulatory and patient communities will propel innovation and real-world applicability. Collaborative data repositories, protocols, and tools will aid in coordinated advancement. Evolution:

  generalfuture work
  Keywords: systems real patient integration world models regulatory attention mechanisms feature importance mapping surrogate modelling artificial
• STRATEGIC EVOLUTION OF PHARMACOVIGILANCE IN THE MODERN HEALTHCARE ECOSYSTEM: A SYSTEMATIC REVIEW AND FUTURE OUTLOOK (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Predictive Pharmacovigilance AI-driven models will identification. enable proactive risk 9.2 Patient-Centric Systems Increased patient involvement in ADR reporting. 9. Lindquist, M. (2008) ‘VigiBase and global drug safety’. 10. McBride, W.G. (1961) ‘Thalidomide and congenital abnormalities’, The Lancet. 11. Sherman, R.E. et al. (2016) ‘Real-world evidence in regulatory decision making’, NEJM, 375(23): 2293–2297. 12. WHO (2023) Pharmacovigilance and Drug Safety.

  generalfuture workevidence 5/5
  Keywords: pharmacovigilance patient global drug safety predictive driven models identification enable proactive risk centric systems increased
• Intelligent Healthcare Tracking System Using Predictive Analytics (2026) · Indian Journal of Computer Science and Technology · doi

  Future work should focus on designing validation studies that meet regulatory standards for safety and effectiveness. Future work will focus on explainable artificial intelligence to build clinician trust, federated learning to combine data across hospitals without violating privacy, contextual awareness to reduce false alarms, edge computing for faster and more private processing, long term trend analysis for chronic disease detection, and regulatory validation through clinical trials.

  generalinline gapsevidence 5/5
  Keywords: future focus validation regulatory designing meet standards safety effectiveness explainable artificial intelligence build clinician trust
• ROLE OF COMMUNITY PHARMACISTS IN ADR MONITORING: A COMPREHENSIVE REVIEW (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Future pharmacovigilance systems will increasingly use electronic health records, artificial intelligence, and digital reporting platforms. Community pharmacists will continue to play a vital role in these systems by ensuring safe medication use and reporting ADRs promptly. The future of Adverse Drug Reaction (ADR) monitoring is expected to evolve significantly with the advancement of pharmacovigilance systems and digital healthcare technologies. The integration of electronic health records, artificial intelligence, and big data analytics will facilitate the early detection and systematic evaluation of adverse drug reactions. These technological innovations can improve signal detection, enhance data accuracy, and enable www.wjpr.net │ Vol 15, Issue 11, 2026. │ ISO 9001: 2015 Certified Journal │ 276 Anju et al. World Journal of Pharmaceutical Research real-time monitoring of drug safety. Furthermore, global collaboration among regulatory authorities, healthcare institutions, and pharmaceutical industries will strengthen international pharmacovigilance networks and improve the sharing of drug safety information. In addition, increasing awareness and education among healthcare professionals and patients will play a vital role in improving ADR reporting in the future. The implementation of mobile applications and online reporting platforms will simplify the reporting process and encourage active participation from both healthcare providers and patients. Personalized medicine and pharmacogenomics may also help predict individual susceptibility to adverse drug reactions, thereby reducing drug-related risks. Consequently, the advancement of innovative pharmacovigilance strategies will enhance patient safety and ensure the safer utilization of therapeutic agents.

  generalfuture workevidence 5/5
  Keywords: reporting drug pharmacovigilance adverse healthcare safety systems reactions enhance future monitoring among professionals improving patient
• Exploring Machine Learning Applications for Genomic Data Analysis in Personalized Medicine (2026) · International Journal of Drug Delivery Technology · doi

  Conclusion The research investigation evaluated machine learning technologies used for genomic analysis with a special focus on individual medical solutions. Available research confirms how advanced deep learning approaches combined with tree-based models increase accuracy levels of predicting diseases while improving pharmaceutical advancement methods. CNNs surpassed traditional ML methods through their performance which yielded a 95.3% success rate in disease classification tasks. SHAP-based analyses feature determined the fundamental genetic variants responsible for disease predisposition which added to the models' interpretability capabilities. The application of Graph Neural Networks in drug discovery showed better results when predicting drug- target interactions because they generated MSE results at 0.012 which surpassed traditional docking algorithms. The results demonstrate AI methodology potential to research while enhancing quicken pharmaceutical targeted medical treatments. Reliable implementation of ML within genomic medicine remains conditional upon solving current data heterogeneity standards together with model understanding requirements and privacy privacy requirements.

  generalrecommendationsevidence 5/5
  Keywords: learning genomic medical based models predicting pharmaceutical surpassed traditional disease drug requirements privacy conclusion investigation
• 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 workevidence 5/5
  Keywords: regulatory systems pharmacovigilance future safety learning drug interdisciplinary journal african alliance advocacy innovation further development
• The Role of Artificial Intelligence in Early Disease Detection Techniques, Applications, Challenges and Future Directions (2026) · International Journal of Science and Research (IJSR) · doi

  Ciona Dewan1, Raghu Raja Mehra2 1Invictus International School, Amritsar Email: cionadewan025[at]gmail.com 2Invictus International School, Amritsar Email: raghu[at]invictusschool.edu.in Abstract: Early and accurate detection of disease is one of the most decisive factors in patient survival, treatment cost and quality of life. Artificial intelligence (AI), and in particular machine learning and deep learning, has emerged as a powerful ally in this effort, capable of analysing medical images, electronic health records, laboratory results and wearable-sensor data with remarkable speed and consistency. This paper reviews the role of AI in early disease detection, surveying the principal techniques, the typical detection pipeline, and applications across cancer, cardiovascular, ophthalmic and neurological disorders. A comparison with conventional diagnostic methods shows that AI systems can match or exceed clinician-level accuracy in several screening tasks while operating at scale. The paper then proposes an integrated, privacy-preserving and explainable framework for clinical deployment, and critically examines the advantages, limitations, and ethical and regulatory challenges involved. Finally, it outlines future directions—including federated learning, explainable AI and continuous wearable monitoring—that could make trustworthy, equitable early detection a routine part of care.

  generalfuture workevidence 5/5
  Keywords: detection learning early disease explainable raghu invictus international school amritsar email artificial intelligence machine deep

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