% Together with your report, you must submit an abstract (via an online form). This abstract must be one paragraph, and may not exceed 200 words. The abstract should provide a concise summary of your project’s purpose, method, and results. Note that the abstracts of all projects may be published on the course website.

Doctors are required to constantly update their knowledge within the medical field, which is largely done by reading medical papers and journals. Rapid increase in publications means it is becoming harder for doctors and medical researchers to filter out what is relevant to their specific work. This study presents a comparative analysis of machine learning (ML) techniques for text classification, aiming to limit the search space for doctors and medical researchers when skimming through medical abstracts. Two approaches will be analyzed: a statistical approach and a Large Language Model (LLM) approach, utilizing OpenAI's GPT-3.5 Turbo. The results of the study show that both approaches could be used in order to assist doctors and medical researchers, although higher accuracy may be required depending on their use. Further, while the LLM approach slightly outperforms, it is not currently worth the additional overhead regarding time and compute. However, great potential for LLMs is shown, and future progress in the LLM field will likely make this approach a more viable option. Other findings include that the difficulty in classifying different categories of abstracts varies, possibly because some categories are not as easily distinguishable linguistically.