Overview of medical analysis capabilities in radiology of current Artificial Intelligence models

Abstract

Judgment is fundamental in medicine, particularly when combining complex data layers with detailed decision-making processes. Radiology processes present a distinct challenge for medical decisions due to the data amount and shortage in time and personnel capable of analyzing images. Additionally, it's crucial to consider each patient's specific situation, including their current state and disease history. Despite advancements in technology, there are still significant hurdles in accurately analyzing radiology data. Radiographic assessments, which are predominantly based on visual inspections, could greatly benefit from enhanced computational analyses. Artificial intelligence (AI) in particular holds the potential to significantly improve the qualitative interpretation of imaging by medical experts - automating and even replacing some parts of their work. This article will be an overview of possibilities and challenges associated with introducing new technology into medical spaces. Doctors are struggling with time and it limits how much care they can show for each patient. The image can be marked for most important parts, AI can produce a more user friendly version of the description, suggesting what might be the problem for later human evaluation. Understanding the possibilities of automating or cutting down time spend by radiology experts on analyze will allow faster deliver of radiologic image description for doctors dealing with patient treatment.

Keywords

• artificial intelligence
• medicine
• radiology
• large language models
• sport
• neural networks