AI in Clinical Applications

Shree Sai Prakashan is writing a full set of B.Pharm titles to this new NEP 2020 syllabus. Get notified the moment the book for this subject is released, or pre-book sets for your college.

Unit 1 · AI in Pharmacokinetics & Dose Optimization 6 hrs

Review of pharmacokinetic parameters (Cmax, Tmax, AUC, clearance); modelling concentration–time relationships with linear and multiple regression for dose adjustment by patient variables (age, weight, renal function); interpreting regression coefficients clinically; error metrics (RMSE, R²); and the limits of linear modelling in nonlinear pharmacokinetics.

Unit 2 · AI in Drug Safety & Pharmacovigilance 6 hrs

Pharmacovigilance systems and real-world data formats (FAERS, EudraVigilance); frequency analysis and signal detection; logistic regression for ADR-risk prediction; the confusion matrix and clinical performance metrics (sensitivity, specificity, precision, recall); and bias and confounding in observational datasets.

Unit 3 · AI in Personalized Medicine & Risk Stratification 6 hrs

The concept of precision medicine; patient covariates and therapeutic response; logistic regression for disease-risk prediction; classifying responders versus non-responders; evaluation metrics in healthcare prediction; and the ethical implications of predictive modelling.

Unit 4 · AI in Clinical Decision Support & Real-World Data 6 hrs

The structure of Electronic Health Records (EHR); AI in Clinical Decision Support Systems (CDSS); regression models for outcome prediction and classification models for risk scoring; real-world data analytics; and the limitations, accountability and interpretability of AI in clinical environments.

Unit 5 · Guided Supervised-Learning Project 6 hrs

A guided project applying regression or classification models to a pharmaceutical or clinical dataset — problem definition, dataset selection, choice of predictor and outcome variables, data preprocessing, model building and evaluation, interpretation of results, and discussion of limitations, bias and ethics.