Real-Time Insulin Dosage Estimation for Type 1 Diabetes Using GPT-4-Turbo

Sammanfattning

Artificial intelligence (AI) is poised to revolutionize healthcare by reducing errors and improving care quality. Large Multimodal Models (LMMs) represent a significant AI advancement, integrating visual and textual data for tasks like image captioning and complex visual reasoning. AI also holds promise in managing diet-related diseases such as Type 1 Diabetes (T1D), where precise insulin dosing and monitoring Carbohydrate (CHO) intake are crucial to prevent severe health complications like Hypoglycemia, Diabetic Ketoacidosis (DKA), and others. This thesis introduces an innovative approach using the LMM, gpt-4-turbo, to estimate CHO content from diverse food images for insulin prediction. Unlike previous studies that broadly analyzed macronutrients or employed different AI approaches, this research specifically focuses on estimating carbohydrates (CHO) from individual food images. It leverages advancements in LMM technology to enhance glycemic control and mitigate medical risks associated with T1D. Methodologically, the thesis employs a quantitative research approach, evaluating gpt-4-turbo's performance through techniques such as Bland-Altman plots, Mean Square Error (MSE), and Mean Absolute Error (MAE). The ’models accuracy improved through fine-tuning LMM, with MAE decreasing from 12.50 grams to 9.35 grams and MSE from 370.40 to 191.87, making the GPT-4 Turbo competitive with baselines and professional assessments. Bland-Altman analysis revealed a slight overestimation bias of 0.81 grams on average. Ideally, metrics should aim for a maximum MAE of 10 grams and MSE of 100, with no average over- or underestimations. While competitive, the model's performance highlights ongoing technical and regulatory challenges in medical applications. Issues like LMM hallucination affect outcome accuracy, emphasizing the need for clear legal guidelines for safe deployment of AI systems in healthcare. Future research should focus on handling outliers such as chicken, fish, eggs, and meat to further improve insulin dosing accuracy and enhance personalized healthcare for individuals with T1D.