APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN PREDICTING DISEASE PROGRESSION AND TREATMENT OUTCOMES

Abstract

Background: Chronic diseases such as diabetes, chronic kidney disease (CKD), and cardiovascular disorders represent a significant burden on healthcare systems worldwide. Early prediction of disease progression and individualized treatment planning are essential for improving patient outcomes. Artificial intelligence (AI) offers promising tools to address these clinical challenges through predictive modeling and personalized care strategies.

Objective: To evaluate the effectiveness of AI in forecasting disease progression and optimizing treatment outcomes among patients with chronic conditions in a tertiary care setting.

Methods: This experimental quantitative study was conducted across tertiary care hospitals in Punjab, Pakistan, from January to August 2024. A total of 330 adult patients with type 2 diabetes, CKD, or cardiovascular disease were enrolled. Clinical data were extracted from electronic medical records, and predictive AI models were developed using supervised machine learning algorithms. Model performance was assessed via accuracy, sensitivity, specificity, and AUC-ROC. Treatment outcomes under AI-assisted care were compared with standard care using parametric statistical tests.

Results: AI models showed high predictive accuracy across conditions: diabetes (91.2%), CKD (88.5%), and CVD (86.4%). Strong agreement was observed between AI predictions and actual clinical outcomes (Cohen’s kappa >0.70 for all). AI-assisted care significantly improved clinical markers, including HbA1c reduction (1.4% vs 0.8%), slowed GFR decline (24.9% vs 16.5%), and greater LDL reduction (27.1 mg/dL vs 18.3 mg/dL), all with statistically significant differences (p<0.005).

Conclusion: AI has substantial potential in predicting disease trajectories and guiding more effective, patient-specific treatment strategies. These findings support its broader integration into precision medicine frameworks.