AI-Driven Clinical Decision Support System for Enhanced Diabetes Diagnosis and Management

TL;DR

This study presents an AI-driven clinical decision support system that accurately diagnoses type 2 diabetes, outperforming non-specialists and showing high agreement with endocrinology experts, thus aiding primary care diagnosis.

Contribution

The paper introduces a hybrid AI-CDSS that combines expert insights with machine learning for improved diabetes diagnosis accuracy.

Findings

AI-CDSS achieved 99.8% accuracy in predicting diabetes.

The system showed 98.8% agreement with endocrinology specialists.

Non-specialists' agreement was 85%, highlighting AI's potential to assist less experienced clinicians.

Abstract

Identifying type 2 diabetes mellitus can be challenging, particularly for primary care physicians. Clinical decision support systems incorporating artificial intelligence (AI-CDSS) can assist medical professionals in diagnosing type 2 diabetes with high accuracy. This study aimed to assess an AI-CDSS specifically developed for the diagnosis of type 2 diabetes by employing a hybrid approach that integrates expert-driven insights with machine learning techniques. The AI-CDSS was developed (training dataset: n = 650) and tested (test dataset: n = 648) using a dataset of 1298 patients with and without type 2 diabetes. To generate predictions, the algorithm utilized key features such as body mass index, plasma fasting glucose, and hemoglobin A1C. Furthermore, a clinical pilot study involving 105 patients was conducted to assess the diagnostic accuracy of the system in comparison to non-endocrinology specialists. The AI-CDSS showed a high degree of accuracy, with 99.8% accuracy in predicting diabetes, 99.3% in predicting prediabetes, 99.2% in identifying at-risk individuals, and 98.8% in predicting no diabetes. The test dataset revealed a 98.8% agreement between endocrinology specialists and the AI-CDSS. Type 2 diabetes was identified in 45% of 105 individuals in the pilot study. Compared with diabetes specialists, the AI-CDSS scored a 98.5% concordance rate, greatly exceeding that of nonendocrinology specialists, who had an 85% agreement rate. These findings indicate that the AI-CDSS has the potential to be a useful tool for accurately identifying type 2 diabetes, especially in situations in which diabetes specialists are not readily available.