Artificial Intelligence in Biomedicine: Systematic Review

Artificial Intelligence (AI) is a rapidly progressing technology with its applications expanding exponentially over the past decade. While initial breakthroughs predominantly focused on deep learning and computer vision, recent advancements have facilitated a shift towards natural language processing and beyond. This includes generative models, like ChatGPT, capable of understanding the ‘grammar’ of software code, analog signals, and molecular structures.

This research undertakes a comprehensive examination of AI trends within the biomedical domain, including the impact of ChatGPT. We explore scientific literature, clinical trials, and FDA-approval data, utilizing a thematic synthesis approach and bibliometric mapping of keywords to examine numerous subsets from over a hundred thousand unique records found in prominent public repositories up to mid-July 2023.

Our analysis reveals a higher prevalence of general health-related publications compared to more specialized papers using or evaluating ChatGPT. However, the growth in specialized papers suggests a convergence with the trend observed for other AI tools. Our findings also imply a greater prevalence of publications using ChatGPT across multiple medical specialties compared to other AI tools, indicating its rising influence in complex fields requiring interdisciplinary collaboration.

Leading topics in AI literature include radiology, ethics, drug discovery, COVID-19, robotics, brain research, stroke, and laparoscopy, indicating a shift from laboratory to emergency medicine and deep-learning-based image processing. Publications involving ChatGPT predominantly address current themes such as COVID-19, practical applications, interdisciplinary collaboration, and risk mitigation.

Radiology retains dominance across all stages of biomedical R&D, spanning preprints, peer-reviewed papers, clinical trials, patents, and FDA approvals. Meanwhile, surgery-focused papers appear more frequently within ChatGPT preprints and case reports. Traditionally less represented areas, such as Pediatrics, Otolaryngology, and Internal Medicine, are starting to realize the benefits of ChatGPT, hinting at its potential to spark innovation within new medical sectors.

AI application in geriatrics is notably underrepresented in publications. However, ongoing clinical trials are already exploring the use of ChatGPT for managing age-related conditions.

The higher frequency of general health-related publications compared to specialized papers employing or evaluating ChatGPT showcases its broad applicability across multiple fields. AI, particularly ChatGPT, possesses significant potential to reshape the future of medicine. With millions of papers published annually across various disciplines, efficiently navigating the information deluge to pinpoint valuable studies has become increasingly challenging. Consequently, AI methods, gaining in popularity, are poised to redefine the future of scientific publishing and its educational reach.

Despite challenges like quality of training data and ethical concerns, prevalent in preceding AI tools, the wider applicability of ChatGPT across diverse fields is manifest.

This review employed the PRISMA tool and numerous overlapping data sources to minimize bias risks.