Medical Artificial Intelligence: Clinical Applications by Specialty

Introduction

What this guide covers

A comprehensive, rigorous and mathematical analysis of Deep Learning and AI systems applied to the clinical field. From ingesting biomedical standards to designing neural architectures for clinical diagnosis, precision medicine and drug discovery.

Overview

Topics covered

• Introduction
• Medical AI Systems Architecture and Data Integration
• The DICOM Standard and Image Ingestion
• FHIR and Structured Clinical Data Exchange
• Multimodal Preprocessing and Normalization Pipelines
• Mathematical and Optimization Fundamentals in Clinical Algorithms

Related concepts

Main connections

• Survival analysis & Kaplan-Meier
• Logistic regression
• Maximum likelihood estimation
• Model interpretability (SHAP / LIME)
• Self-attention & Transformer architectures
• Neural networks & deep learning

Compatible notation

Mathematical sample in LaTeX

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