Universitat Internacional de Catalunya

Artificial Intelligence in Biomedicine from Basics to Applications

Artificial Intelligence in Biomedicine from Basics to Applications
4
16422
4
First semester
op
Main language of instruction: English

Other languages of instruction: Catalan, Spanish

Teaching staff


Dr. CHOROSTECKI, Uciel Pablo - upchorostecki@uic.es

  • Teachers

    • Uciel Chorostecki

    • Adrià Fernández

    • Robert Subirana

Introduction

It introduces students to the fundamental concepts and practical applications of artificial intelligence in biomedical research and healthcare.

Sustainable Development Goals (SDGs): The course on Artificial Intelligence in Biomedicine contributes to the Sustainable Development Goals (SDGs) of the 2030 Agenda, particularly SDGs 3, 9, 10, 12, and 17. It does so by promoting health and well-being, fostering biomedical innovation and infrastructure, supporting social equity through access to scientific data, ensuring responsible consumption of computational resources, and encouraging scientific and social progress through global partnerships in data sharing.


Pre-course requirements

Successful completion of the Introduction to Bioinformatics and Biomolecules Interaction courses

Objectives

  • Understand the principles of generative AI and its biomedical applications

  • Comprehend machine learning and deep learning concepts

  • Evaluate machine learning models and pipelines

  • Analyze AI applications in genomics, single-cell analysis, and drug discovery

  • Discuss ethical implications, interpretability, and governance of AI in medicine

Competences/Learning outcomes of the degree programme

  • CN14 - Identify the principles of biomedical sciences related to health, as well as the basic concepts and tools that have an impact on Biomedical Sciences and allow them to work in any of its fields (biomedical companies, bioinformatics labs, research laboratories, clinical analysis companies, etc.).
  • CP05 - Apply biological foundations in the search for practical solutions to health problems, following ethical standards and scientific rigour and respecting fundamental equal rights between men and women, and the promotion of human rights and the values inherent in a peaceful society of democratic values that includes inclusive, non-discriminatory language without stereotypes.

Learning outcomes of the subject

At the end of the course, students will be able to apply and critically evaluate generative AI and machine learning models to solve biomedical challenges, while navigating their ethical and practical implications.

Syllabus

Block 1: The AI We All Use – Generative Intelligence in the Biomedical Era (8 hours)

  • Starting with ChatGPT, Claude, and DeepSeek

  • Generative AI concepts

  • Large Language Models including tokens, prompts, and training data

  • Biomedical use cases like summarization, literature review, and coding assistance

  • Hands-on exploration interacting with AI models

  • Critical use covering hallucinations, bias, and responsible prompting

  • Discussion on generative AI support for biomedical learning and research

Block 2: From Data to Decision – What AI Actually Is (8 hours)

  • Demystifying AI concepts before code

  • Artificial Intelligence definition

  • Machine Learning and Deep Learning definitions and differences

  • Patterns, inference, and data representation

  • Supervised, unsupervised, and reinforcement learning

  • Machine learning pipeline from data to evaluation

  • Biomedical case examples like cancer risk scoring and disease classification

Block 3: How Machines Learn – Algorithms Behind the Scenes (8 hours)

  • Learning without the math using intuition and tools

  • Simplified data representation including features, matrices, vectors, and embeddings (4 hours)

  • Simplified core supervised algorithms including logistic regression, decision trees, random forests, and SVMs

  • Basic neural network architecture and learning dynamics

  • Model evaluation covering accuracy, sensitivity, specificity, and AUC (4 hours)

Block 4: Learning from Images, Genes and Cells – Biomedical Applications of AI (8 hours)

  • AI in genomics, multi-omics, and single cell

  • Hit identification via AI chemoinformatics, structural biology, and docking

  • Lead optimization predicting affinity, toxicity, ADME, and patient stratification

  • Healthcare foundational models, imaging, wearables, lab robots, and the case of Recursion

Block 5: Ethics, Interpretability and the Future of AI in Medicine (6 hours)

  • AI explainability through linear coefficients, feature importance, Shap, and GNN

  • AI geopolitics, sustainability, ethics, governance, and open source

  • Hands-on AI debate regarding patients in hospitals

Teaching and learning activities

In person



  • 38 hours combining Lectures (CM) and Case Methods (MC), integrating theoretical presentations with hands-on exercises.

  • 2 hours for the midterm exam

Evaluation systems and criteria

In person



First Call

  • 20% Case method sessions

  • 30% Midterm exam

  • 40% Final exam

  • 10% Subjective criteria based on involvement and participation

Multiple Choice Exam Rules

If the exam consists of multiple-choice questions (test type), according to the degree regulations, for every +1 point for a correct answer, points will be deducted for incorrect answers as follows:

  • -0.33 points if there are 4 response options (with one correct).

  • -0.25 points if there are 5 response options (with one correct).

Second Call

  • 75% Final exam

  • 25% Maintained case method and subjective grade

General Evaluation Rules

  • Minimum grade of 5 on the final exam

  • General average of 5 or higher

  • 75% minimum attendance

  • Inappropriate use of electronic devices may result in expulsion

Bibliography and resources

  • Biomedical Informatics, 4th Edition. Springer (2014). ISBN: 978-1-4471-4473-1

Print version