Exploring how LLMs can analyse patient data, medical literature, and symptoms to assist in the early detection of chronic conditions like diabetes, cancer, or cardiovascular diseases

Exploring how LLMs can analyse patient data, medical literature, and symptoms to assist in the early detection of chronic conditions like diabetes, cancer, or cardiovascular diseases.

Antony Ronald Reagan Panguraj

Email: antony.reagan@gmail.com

Abstract

That is why timely diagnostics of chronic diseases like diabetes, cancer, and cardiovascular diseases is vital for patients’ outcomes and healthcare economical effectiveness. Since dysplasia’s are notorious for being challenging to diagnose, detecting them at early stages makes treatment easier, management better and, therefore, higher survival rates than if diagnosed at advanced stages. Current technologies, widely recognized as Artificial Intelligence (AI), and more recent additions such as Large Language Models (LLMs), hold great promise for shifting the paradigm of early disease diagnosing. Due to the capability to crunch huge data on individual patients, medical research, and symptoms LLMs enable the healthcare providers to detect correlations that may not be conspicuous.

Taken from aspects of EHRs, lab results, medical imaging, and patient’s reported symptoms, LLMs can help detect diseases at an early stage. These models can also include, summarize and compare the given data and, based on the large database of medical data, to determine the potential development of such diseases as diabetes mellitus, cancer, cardiovascular diseases at an early stage. LLMs are capable of making diagnostic predictions through the comparison with huge datasets of medical literature, which would help clinicians narrow down the population at highest risk of a certain condition even before developing clinical manifestations of that particular disease.

For instance, an LLM could process a patient’s medical history, lab results, genetics and lifestyle and come up with an early onset alert of diseases such as diabetes that are usually hard to detect when they first occur. In the case of carcinogenic formation, the AI models can analyse medical imagery data and distinguish minor abnormality that may call for carcinogenic formations. Likewise, in cardiovascular disease, LLMs can identify the risk of heart disease occurrence or a likelihood of stroke from vital statistics and biomarkers.

Incorporation of LLMs into processes of early disease diagnosis has the potential of enhancing the efficacy of health systems. This, in turn can help LLMs speed up the diagnosing process, individualized treatment, and disease prevention, alleviating the pressure off the healthcare’s systems and enhance the quality of life of people living with such illnesses. In extent to the technological progress of AI in the future, its use in early disease diagnosis will hence increase, resulting in personalized and timely approaches.

Key Words: AI, Early Disease Detection, Large Language Models, Chronic Conditions, Diabetes, Cancer