This course provides important knowledge about the principles of artificial intelligence (AI) and machine learning (ML) applied to the field of healthcare engineering. Key techniques such as classification, regression, and clustering will be explored, with a focus on solving biomedical problems, such as medical image interpretation, clinical outcome prediction, and physiological signal analysis. Students will develop practical skills in implementing models using programming environments, integrating technical knowledge with the specific needs of the healthcare sector. In addition, the ethical and regulatory challenges associated with the use of AI in healthcare settings will be analyzed, fostering a critical understanding of model reliability and interpretability. Upon completion, participants will be prepared to apply these tools to develop innovative solutions that contribute to improving disease prevention, diagnosis, and treatment, as well as optimizing healthcare management.
Titular Professors
Knowledge of linear algebra, mathematics, statistics, programming, signal processing, and medical imaging is recommended.
Students acquire the knowledge and develop the skills listed below: 1. Understand the fundamentals of AI and machine learning applied to biomedical data analysis and clinical process improvement. 2. Develop and evaluate AI models to solve specific problems in the healthcare field. 3. Apply programming tools to implement AI algorithms, using environments and libraries such as Python, TensorFlow, Keras, or Scikit-Learn. 4. Identify and address ethical and regulatory challenges related to the use of AI in healthcare settings, ensuring data privacy and security. 5. Encourage interdisciplinary work between engineering professionals and the healthcare sector, promoting innovative and evidence-based solutions.
1. Introduction to Artificial Intelligence (AI)
• Definition and applications of AI, history, evolution, and types. Differences between AI, Machine Learning (ML), and Deep Learning.
2. Machine Learning Fundamentals
• Definition of ML and its types (supervised learning, unsupervised learning, reinforcement learning). Basic concepts: models, features, overfitting and underfitting. Model evaluation and metrics. Libraries and tools in Python.
3. Supervised Learning
• Linear and logistic regression. Support vector machines (SVMs). Decision trees and random forests. Artificial neural networks (multilayer perceptrons). Practical applications.
4. Unsupervised Learning
• Clustering (K-means, DBSCAN: Density-Based Spatial Clustering of Applications with Noise, Hierarchical). Dimensionality reduction (PCA: Principal Component Analysis, t-SNE: t-Distributed Stochastic Neighbor Embedding). Practical applications.
5. Deep Learning
• Introduction to deep neural networks. Architectures: Convolutional Neural Networks. Recurrent Neural Networks. Transformers. Applications in computer vision and natural language processing. Implementation with TensorFlow/PyTorch. Generative models. Generative adversarial networks (GANs) and VAEs: Variational Autoencoders).
6. Ethics, Bias, and the Future of AI.
The course combines theoretical and practical approaches to ensure comprehensive learning, with an emphasis on the application of artificial intelligence in healthcare settings. It is structured around theoretical lectures, practical sessions, analysis and discussion of real-life AI application cases in healthcare engineering, and the development of a project where students apply the acquired knowledge to solve a specific healthcare problem.
The assessment elements of the subject are: Continuous assessment (10%), Midterm exam (20%), Final exam (20%) and Projects (50%).
It will be assessed:
- The understanding of the fundamentals of Artificial Intelligence, its evolution, typologies, and the differences between AI, Machine Learning, and Deep Learning, as well as their application in the field of health engineering.
- Mastery of the principles of Machine Learning, including learning paradigms, model concepts, features, overfitting and underfitting, and the correct use of evaluation metrics.
- The ability to apply supervised learning techniques, such as regression, SVM, decision trees, and neural networks, to real biomedical problem-solving.
- The ability to use unsupervised learning techniques, including clustering and dimensionality reduction (PCA, t-SNE), and to correctly interpret patterns extracted from data.
- The understanding and application of Deep Learning models, including convolutional, recurrent, and transformer architectures, as well as generative models, in contexts such as medical imaging and signal analysis.
- Proficiency in the practical implementation of models using Python tools and libraries (such as TensorFlow or PyTorch), as well as in the correct interpretation of the results obtained.
- The ability to organize, manage, analyze, and present information and knowledge in a structured, clear manner adapted to the scientific and healthcare context.
- Critical reflection on the ethical aspects, biases, reliability, interpretability, and regulatory framework of the use of AI in healthcare environments, assessing its present and future impact.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
Python 3 Documentation. Retrieved from https://docs.python.org/3/ Pandas Documentation. Retrieved from https://pandas.pydata.org/pandas-docs/stable/ Matplotlib Documentation. Retrieved from https://matplotlib.org/contents.html Seaborn Documentation. Retrieved from https://seaborn.pydata.org/api.html Scikit Learn Documentation. Retrieved from https://scikit-learn.org/stable/documentation.html Keras Documentation. Retrieved from https://keras.io/ Pytorch Documentation. Retrieved from https://pytorch.org/