CH 01. Future Doctors Growing with AI: An Essential Guide for Medical Students and Residents

I.
Introduction: What does our future hold?
II. Becoming a Healthcare Professional in the AI ​​Era: A Roadmap for Residents, Medical Students, and Developers
III.
Introduction to Medical AI: A Guide to Installing Python and Using Google Colab
IV.
AI Development Environment Setup Guide for Medical Researchers

CH 02.
Learn Python, the Key to Clinical Research and Data Analysis

I.
Python Basics for Medical Information Processing: Data Types and Structures
II.
A New Approach to Medical Formulas: Understanding the Anion Gap in Python
III.
Differential Diagnosis of Hyperkalemia in Python: From TTKG to Algorithms
IV.
Implementing a Cushing Syndrome Diagnostic Algorithm in Python: Coding Complex Medical Knowledge
V.
Implementing a CHA2DS2-VASc Score Calculator in Python: Using For and While Loops
VI.
A hyperkalemia management system implemented with Python classes and modules.
VII.
Doctors' New Partner, AI: A Journey to Healthcare Innovation Starting with Python

CH 03.
Everything About Medical AI: From Fundamentals to Cutting-Edge Technologies

I.
The 7-Stage Pipeline for Healthcare AI Development: From Data Mining to Model Deployment
II.
Fundamentals of Medical AI: Understanding the Core Concepts of Machine Learning and Deep Learning
III.
Essential Tools for Medical AI Development: A Comprehensive Guide to Key Frameworks
IV.
Machine Learning in Healthcare: Key Techniques and Clinical Applications
V.
Deep Learning Driving Healthcare Innovation: Key Techniques and Clinical Applications
VI.
The Latest Trends in Medical AI: Cutting-Edge Technologies and Tools

CH 04.
The Starting Point of Medical AI Development: From Environment Creation to Resource Exploration

I.
Exploring Medical AI Code for Medical Students and Residents: Using Github, Hugging Face, and Papers with Code
II.
A Guide to Medical AI Datasets for Medical Students and Residents
III.
A Game Changer in Medical AI Development: The Uses and Potential of LLMs

CH 05.
Deep Learning for Video Image Analysis

I.
MedMNIST and MONAI: An Effective Entry Point for Medical AI Research
II.
No Specific Change? AI Solves the Challenge of Chest X-ray Interpretation
III.
Beyond Complete Anatomy: Personalized 3D Human Models Powered by AI
IV.
AI's Eyes on Muscle: For Precise Evaluation of Orthopedic Surgery
V.
From 30-Second Pressure to Automated Analysis: Where Medical Education Meets Technology
VI.
The Essential Tool for General Practitioners: The Synergy of Ultrasound and AI
VII.
MediaPipe BlazePose: Real-time pose estimation technology revolutionizing motion analysis in the medical field.

CH 06.
Deep Learning for Time Series Data Analysis

I.
Escaping the 7 AM Nightmare: Revolutionizing Blood Sugar Management with Deep Learning
II. AI's Stethoscope: Reading the Heart's Rhythm with Deep Learning

CH 07.
Generative AI and Natural Language Processing

I.
A New Paradigm for Medical Records: The LLaMA Model Meets AI
II. The Evolving Medical School Genealogy with AI: The GPT-4 Smart Learning Revolution

CH 08.
Medical GitHub: An AI Playground for Future Doctors

As an orthopedic surgeon and professor researching artificial intelligence, I have witnessed firsthand the potential and challenges of AI in the medical field.
With the advent of ChatGPT, artificial intelligence has emerged as a hot topic in our society, and discussions about the role and potential of artificial intelligence are also actively taking place in the medical field.

While working in the field, I've been deeply contemplating how artificial intelligence will transform the future of medicine and how our medical professionals should respond to these changes.
It was particularly disheartening to see young medical professionals, despite their deep interest in artificial intelligence, struggle to actually learn and apply it.

"Doctor, Meet Code" started from these concerns.
I worked with practicing medical AI developers to create a practical guide that would be accessible to my colleagues in the medical field.
This book systematically covers everything from the basic concepts of artificial intelligence to practical applications in medical settings, from a medical professional's perspective.

In particular, the dedicated efforts of resident Dr. Park Hyun-woo helped transform this book from a practical guide on medical artificial intelligence into a textbook that even novice doctors can easily understand.
Additionally, researcher Hyunsu Kim's contribution was significant, as he systematically organized the codes used in actual research and created a practical workbook.
Additionally, the hard work of our GAIME Lab members, who have always silently collected, analyzed, and coded data for research, has greatly enhanced the quality of this book.

This book will provide practical assistance to medical students, residents, specialists, and medical researchers in understanding and utilizing artificial intelligence.
Furthermore, I hope that this will serve as a crucial foundation for our medical community to lead the era of artificial intelligence.