Okay, I need to create a Wikipedia-style document in markdown format based on the given categories and keywords. The main topic is "의료 분석" under the subcategory of machine learning in technology. Let me start by understanding the requirements.

First, the structure: H1 for the title, an overview section, several sections with H2 and H3 headings, and possibly references or related documents. The content needs to be accurate, objective, detailed enough, easy to read, and use technical terms with explanations. Also, markdown formatting with proper hierarchy, lists, tables, emphasis, and code blocks if needed. The document should be at least 800 characters long.

Starting with the title: "의료 분석" as H1. Then an introduction that explains what medical analysis is in the context of machine learning. Next sections could include applications, techniques, challenges, case studies, and future directions. Each section can have sub-sections (H3).

For the overview, I should define medical analysis using ML, mention its importance in healthcare. Then under applications, maybe talk about disease diagnosis, drug discovery, personalized treatment. Techniques could cover supervised learning, unsupervised learning, deep learning. Challenges might include data privacy, model interpretability, integration with clinical workflows. Case studies can have examples like cancer detection or predictive analytics. Future directions could discuss AI ethics, collaboration between disciplines.

Need to ensure technical terms are explained. For example, when mentioning supervised learning, briefly explain it's a type of ML where models learn from labeled data. Also, include some specific algorithms like CNNs for image analysis in medical imaging.

Check if there are any code examples or tables needed. Maybe a table comparing different ML techniques used in healthcare. Or a code block showing a simple example of a model using Python libraries like TensorFlow or PyTorch. But since the user didn't specify, maybe just mention them without actual code unless necessary.

Make sure each section flows logically. Avoid being too technical but still professional. Use bullet points for lists where appropriate. Also, include references to related documents or further reading if possible.

Now, check the length. The initial draft should be around 800 characters. Let me count: the example provided in the user's previous response was about 1200 words, so I need to make sure mine is at least that. But since this is a thought process, maybe not worry too much about the exact number here.

Potential sections:

• Introduction
• Applications of ML in Medical Analysis
• Disease Diagnosis
• Drug Discovery
• Personalized Treatment
• Techniques and Algorithms
• Supervised Learning
• Unsupervised Learning
• Deep Learning
• Challenges and Limitations
• Data Privacy
• Model Interpretability
• Clinical Integration
• Case Studies
• Example 1: Cancer Detection with CNNs
• Example 2: Predictive Analytics for Chronic Diseases
• Future Directions
• References

Each of these sections can have H2 headings, and sub-sections as H3. Need to elaborate each part with details, examples, explanations.

Make sure the language is clear and concise. Avoid jargon without explanation. For instance, when discussing deep learning, explain it's a subset of ML using neural networks with multiple layers.

Check for accuracy: Ensure that the information about ML applications in healthcare is up-to-date and correct. Mention real-world applications like IBM Watson, Google Health, etc., if relevant.

Also, consider including statistics or studies to back up claims, but since this is a general overview, maybe not too specific unless necessary.

Finally, review the markdown structure for proper headings, lists, and formatting. Ensure that code blocks are used appropriately if any examples are included.