AI in Healthcare: The Complete Practitioner's Playbook

• Clarify that AI excels at narrow, well-defined tasks and is not general intelligence .

• Dispel the myth that AI replaces doctors, showing instead how it augments them by handling repetitive tasks at speed and scale .

• Define AI as software trained on data to optimize a mathematical function, focusing on pattern recognition .

• Define core concepts: Data, Model, Training, Evaluation, Labels, and Task.

• Understand Datasets, Overfitting, the limits of Accuracy, and the high cost of Annotation .

• Explore Generative AI concepts: LLM, Prompt, Token, Context Window, and Temperature .

• Understand RAG (Retrieval-Augmented Generation), Fine-Tuning, and the critical risk of Hallucinations .

• Define measurable Features and Loss Functions .

• Explore Transfer Learning, Augmentation, Epoch, Batch Size, Regularization, AUC-ROC, and the Confusion Matrix .

• Review current dominance: Computer Vision in medical imaging and LLMs for clinical notes.

• Identify frontier technologies: Multimodal AI, Edge AI for point-of-care, and Agentic AI.

• Talk about the inverted pyramid for modality of diagnostics

• Talk about the application of AI in radiology, a shallow dive into computer vision

• Explore the 10+ verticals where AI touches every corner of healthcare, far beyond just reading scans

• Examine how AI compresses the 10-15 year drug discovery timeline via molecular generation and toxicity prediction

• Explore real-time anatomical structure identification and robotic surgery motion planning

• Learn how AI recruits patients from EHR data and predicts trial endpoints

• Understand AI-enhanced virtual consultations and real-time symptom analysis

• Understand how Federated Learning allows multi-site training by sending the model to the hospital without moving patient data, ensuring privacy compliance

• Explore digital pathology AI analyzing gigapixel tissue slides, highlighting companies like PathAI, Paige, and Aiforia

• Review real-world Indian case studies: Qure.ai (TB screening), SigTuple (blood smears), and Tricog (cardiac emergencies)

• Break down the responsibilities for ML Engineers, Data Scientists, and Research Scientists

• Understand the value of becoming a "Bridge Person" who speaks both clinical and technical languages

• Review entry-level and experienced salary expectations across India, the US, and the UK for each job role

• Navigate the sequential learning path: Python → Math → Data Science → ML → DL → CV → NLP → GenAI → Agentic AI

• Detail the specific tools, outputs, and time estimates for mastering fundamentals through to generative models

• Follow the domain knowledge shortcut for non-bio backgrounds: Anatomy → Medical Terminology → Fields of Medicine → Disease Understanding → Imaging Modalities

• Apply the 5-step framework: Understand the Disease, Clinical Manifestation, Current Workflow, AI Intervention Point, and Data Ecosystem

• Know the key pointers that would separate you from the others.

• In depth analysis of metrics and why accuracy doesnt matter all the time.

• Talk about the data infrastructure on a hospital environment.

• Talk about the need of regulation- why cant you deploy your model directly!!
  Introduction to the various regulatory bodies and data privacy bodies

• Go through the various classes of risks in Software as a Medical Device

• Understand the components of a regulatory submission
  

• Understand the common challenges of AI in healthcare

• Go through a case study to understand ethical principles and bias mitigation strategies

• Walk through various explainable AI techniques

Talk about my journey, right from a biomedical engineer to AI in healthcare R&D Engineer. Go through the key principles and lessons from my journey.