Systematic literature reviews and meta-analyses represent the highest quality of evidence in pharmaceutical research, essential in shaping public policies and clinical guidelines. Rigorously prepared by subject matter experts, these comprehensive reports are foundational in the research and development of pharmacotherapies and medical devices. Currently to meet the highest standard of evidence, the process of conducting a single systematic literature review is exorbitantly time intensive, with a team of experts dedicating on average 6-months to 1 year to complete a review. Artificial intelligence (AI) is proposed as a solution to streamline the process and reduce costs, yet the general AI approach is burdened with several inherent limitations. Under this general AI approach are large language models and chatbots, which have interesting applications yet pose a significant risk for pharmaceutical research and pharmacy practice. Where a minor deviation in accuracy may be acceptable in other industries, it could mean the death of a patient in healthcare. Explainable AI is a breakthrough at the nexus of computer science and healthcare, offering superior accuracy and easy to understand models for experts to efficiently conduct their critical work. Researchers such as Joshua Morriss, Ph.D. has witnessed the benefit of specialized explainable AI systems over generalist AI models. Dr. Morriss has spent the last 4 years developing explainable AI systems for biomedical research. One such system is the Literature Review Network (LRN), the validated and complete Explainable AI platform for literature reviews, which is already producing savings for pharmaceutical researchers and changing the timeline for research to be completed in minutes, not months. Join Dr. Morriss as he discusses the differences in general AI versus explainable AI and their impact in healthcare.