MICROCREDENTIAL I: BASIC FUNDAMENTALS OF AI FOR HEALTHCARE (40 teaching hours)

• Introduction: What is AI? Strong AI, Weak AI, and the Singularity. Some areas of AI. Machine learning and deep learning. Natural Language Processing. Generative AI. Language models. Explainable, responsible, and ethical AI. Data sources for AI in healthcare. Areas of application for AI in healthcare. Challenges of AI in healthcare. Total 5 hours. José M. Sempere.
• Machine learning: Main machine learning techniques: artificial neural networks, random forests, Support Vector Machines, etc. Regression techniques. Clustering. Ensemble learning: bagging and boosting. Model training and validation. Overfitting. Evaluation metrics for machine learning experiments.
• Natural Language Processing.
  • Introduction to NLP.
  • Historical evolution of NLP.
  • NLP applications: Information retrieval, information extraction, information classification, disambiguation, opinion mining, simplification, and summary generation.
  • Lines of work in NLP: Domain adaptation and use cases.
  • NLP ecosystem.

• • Linguistic resources: levels of analysis and corpus.

• • Language Models.
    • What is a language model?
    • How do LLMs learn?
    • Methods for evaluating LLMs.
    • Solving specific tasks with LLMs.
    • Practical examples of the use of LLMs.

• • • Applications of NLP in healthcare and biomedicine.
      • Why is NLP relevant in biomedicine? What specific challenges does it face in this field?
      • Generative Artificial Intelligence applied to biomedicine.
      • Prompt engineering (Prompting).
      • Retrieval-Augmented Generation (RAG)
      • Virtual assistants for biomedicine.
      • Observation of trends in biomedicine: T2Know.