AI integrates genetic, lifestyle, and environmental data to predict optimal treatment for each individual and precisely design diagnosis, prognosis, and drug response.
By examining the current state and limitations of clinical applications, explainability, bias, privacy, and the principles of human intervention, we present implementation strategies that go beyond "medicine for average."
It covers data standardization and interoperability, digital twins and adaptive clinical trials, and even a domestic application roadmap, all in one volume.
Artificial Intelligence Encyclopedia.
You can find the artificial intelligence knowledge you need at aiseries.oopy.io.

The history of medical AI is longer than we think, and its impact is deeper.
Lessons learned from past failures, insights gleaned from successes, and a relentless spirit of challenge have become the foundation of today's medical AI.
The explainability of expert systems, the practicality of CAD systems, and the adaptability of machine learning are all baked into today's AI systems.
Next, we will examine in detail the performance of current medical AI, created through this long history, in actual clinical practice.
Along with the incredible advancements brought about by the deep learning revolution, we will also address the challenges that still remain.
By understanding the past, we can better prepare for the future.
--- From “01_“History of Medical Artificial Intelligence””

Recently, artificial intelligence has been increasingly utilized in predicting the structure of disease-related receptors and in the search for new drug candidates.
For example, AlphaFold-based structural prediction of the GLP-1 receptor, a key target for the treatment of type 2 diabetes and obesity, has enabled understanding of drug mechanisms of action and the design of novel agents. The development and utilization of AI drug design platforms are attracting attention, and examples such as the design and activity verification of semaglutide analogs are emerging. AI is supporting efficiency improvements across multiple stages of new drug development, from protein structure-based target prediction, drug candidate discovery, and mechanism of action exploration.
Its potential is rapidly expanding, especially in the fields of complex metabolic diseases and chronic diseases.
--- From "03_“Genome Revolution: The Foundation of Precision Medicine”"

The medical field is undergoing a paradigm shift from ‘standard treatment for the average patient’ to ‘personalized treatment for individual patients.’
At the heart of this transformation are genomics, biomarker discovery, and multi-omics approaches, with pharmacogenomics and AI-based drug development emerging as key tools to make this happen.
The one-size-fits-all approach of traditional medicine is showing its limitations, and precision treatment that comprehensively considers an individual's genetic characteristics, molecular profile, living environment, and disease status is now becoming a reality.
In particular, rapid advances in genome sequencing technology, computer-based analysis of molecular biology data, and AI-enabled multidimensional data integration are forming the core foundation of personalized medicine.
--- From "06_“The Era of Customized Treatment: Personalized Medicine Designed by AI”"

We examine the ethical and security aspects that must be considered as precision medicine expands.
As precision medicine expands from population data to personalized treatments, advances in genomic medicine in particular present unprecedented opportunities while also raising complex ethical and social questions.
As individuals' most sensitive information is processed on a massive scale by AI systems, a balanced approach that protects human-centered values ​​alongside technological innovation is needed.

--- From “09_“Ethics and Security””