Artificial Intelligence in Allied Health Sciences Education and Research in Pakistan: Navigating Innovation, Academic Transformation, and Emerging Dependency
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Background: Artificial intelligence is increasingly transforming healthcare education and research by supporting personalized learning, simulation-based training, literature retrieval, scientific writing, data analysis, and clinical decision support. In allied health sciences, these applications may enhance academic productivity, practical training, and access to evidence-based knowledge, particularly in resource-variable settings such as Pakistan. Objective: This narrative review aimed to synthesize current evidence on the role of artificial intelligence in allied health sciences education and research in Pakistan, with emphasis on educational applications, research utilization, academic dependency, ethical concerns, institutional barriers, and responsible implementation. Methods: A narrative review was conducted using literature published between 2017 and 2026 from PubMed, Scopus, Web of Science, Google Scholar, and relevant international policy and editorial guidance documents. Evidence was synthesized thematically across educational, research, ethical, institutional, and Pakistan-specific implementation domains. Results: The review identified AI-supported education, research assistance, clinical training, academic dependency, integrity risks, data privacy, faculty preparedness, infrastructure disparities, and policy gaps as major themes. AI may improve adaptive learning, simulation, academic writing, evidence synthesis, and research productivity; however, excessive reliance may weaken critical thinking, originality, clinical reasoning, and independent scholarship. Conclusion: Artificial intelligence should be integrated into allied health sciences education and research in Pakistan as a supervised, transparent, and ethically governed support tool. Institutional AI policies, faculty development, student AI literacy, assessment reform, data governance, and local validation are essential to ensure responsible and equitable implementation
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