Artificial Intelligence in Healthcare: 2024 Year in Review
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
With over a thousand FDA-approved artificial intelligence/machine learning-enabled medical devices, research and publications is maturing from focusing on the development and internal validation of models to the external validation of models and implementation trials. Foundation models, especially Large Language Models, have spurred additional aspects of AI research related to healthcare, especially with the use of text-based data to address healthcare education and administrative tasks related to patient care.
Methods
We performed a PubMed search using the terms “machine learning” or “artificial intelligence” and “2024,” restricted to English language and human subject research on January 1, 2025. Utilizing a deep learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics. We also performed a detailed analysis of the distribution of foundation model-based publications amongst the healthcare specialties.
Results
For the year 2024, the PubMed search yielded 28,180 articles, of which 1,693 were classified as mature using a BERT model. Following exclusions, 1,551 articles were selected for the final data analysis. Amongst these, the highest number of articles in each specialty originated from Imaging (407), Head and Neck (127), and General (122). The analysis of data types revealed that image data (903 [57.0%]) was still the predominant data type, but the use of text data (525 [33.1%]) had substantially increased. Additionally, we also found that LLMs (479) and AI General (448) category models have overtaken deep learning models (372) in healthcare AI research. For LLM-related publications, we are seeing increasing trends in research related to healthcare education and administrative tasks.
Conclusion
With the introduction of foundation models, healthcare research trends are changing. The adoption of LLMs and text data types amongst various healthcare specialties, especially for education and administrative tasks, is unlocking new potential for AI applications in healthcare.
