Artificial intelligence (AI) is rapidly reshaping clinical research and healthcare delivery, yet meaningful adoption depends less on technical mastery and more on cultivating AI literacy among clinicians and research staff. Building a foundational understanding of concepts such as supervised learning, bias, and model validation enables professionals to interpret and apply AI-generated insights with confidence. Familiarity grows through exposure to AI-enabled electronic medical record systems, interdisciplinary collaboration with data scientists and informaticists, and small pilot projects that demonstrate workflow fit and real-world value. Prioritizing transparency, explainability, and ethical considerations ensures that AI augments clinical decision-making. Practical applications already span oncology research, pharmacovigilance, and operational efficiency, from predictive models informing disease management to natural language processing tools automating safety reporting and remote monitoring systems reducing administrative burden. Ultimately, AI fluency empowers clinicians and research teams to lead the integration of these technologies and enhance patient-centered care.
Sheth et al. (Sun,) studied this question.