Major
Biological Sciences
Advisor
Plouffe, Brian
Advisor Department
Cell and Molecular Biology
Date
4-2024
Keywords
Artificial Intelligence; Medicine
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.
Abstract
The integration of technology like artificial intelligence (AI) in medical diagnosis offers a unique solution to the growing demands of healthcare providers across all fields of medicine. The purpose of the literature review is to examine current and future applications of artificial intelligence in healthcare, as well as associated challenges to implementing AI in medical decision-making and care access. The literature review was organized into sections examining current applications, limitations, and future directions. From the literature review conducted, I found that AI technology like machine learning (ML) and deep learning (DL) have the potential to optimize fields like medical diagnostics, through quickly interpreting lab and imaging results, offering efficient differential diagnoses for physicians to make the final call on. Through this process, I also found limitations to the current AI research, specifically related to concerns for bias and misinformation with AI. As such, the use of AI should be strongly regulated and designed to avoid biases so that every person equal access to care. Based on the research, it is important to develop a system that incorporates anonymized patient data, including considerations for the social determinants of health and other healthcare access disparities, to develop a system that can be used on a large scale. It may be pertinent to test these measures on a small scale, like with private practices, before expanding to hospitals and across healthcare systems. Ultimately, AI software relies solely on the input of information, so it is important that the software is designed to account for a wide variety of patients, diseases, diagnoses, and treatment plans curated to the individual.