Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course BMEN35F valid from Autumn 2022

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  • The course provides basic knowledge in the field of artificial intelligence and machine learning for applications in medicine and health. The course covers the chain from medical databases via algorithms to regulations and requirements for diagnostic software.
  • Areas covered are:

    - Introduction of artificial intelligence in healthcare applications

    - Overview of machine learning algorithms and methods

    - How to choose ML methods for different applications

    - How to select settings and optimize performance

    - How to evaluate performance

    - Regulatory, social, ethical and legal issues regarding artificial intelligence in medicine

    -State-of-the-art AI that is applied to important medical fields such as ECG, neurology, biomedical imaging, heart sound, oncology, diabetes, etc.

    Practical work:

    - Introduction to Python / Jupyter / Colab (basics, linear algebra, plotting)

    - Linear models

    - Measurement values and visualization

    - Trees and knn

    - Ensemble methods

    - Neural networks (shallow, MLP, introduction to Keras / Tensorflow)

    - Deep Neural Networks (CNN)

    - Deep Learning (LSTM / RNN)
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • be able to and understand and have an overall picture of how machine learning and artificial intelligence can be designed for different medical issues.
    be able to apply the most common methods to real problems and real medical signals (Python)
    know and understand how special requirements and regulations for diagnostic systems and other medical devices affect the design and validation process.
Competences and Skills
  • For a passing grade the doctoral student must
  • be able to understand the characteristics of the most common machine learning methods.
    be able to understand what settings need to be made for different methods.
    be able to understand how properties of medical training and validation data affect the performance of the methods.
    be able to choose the appropriate method for a described situation.
    be able to overview the consequences of different method choices and evaluation strategies that can be chosen for different types of problems
    be able to use available toolboxes for machine learning and in this way solve practical problems in Python.
Judgement and Approach
  • For a passing grade the doctoral student must
  • have the ability to analyze and evaluate different machine learning algorithms, as well as interpret and describe their inherent principles.
    have insight into how the transparency and generalizability of different methods make them more or less suitable in different medical contexts.
Types of Instruction
  • Lectures
  • Seminars
  • Exercises
Examination Formats
  • Written exam
  • Computer assignments
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
Selection Criteria
  • Österberg, M. & Lindsköld, L.: AI for Better Health. 2020.
    Lindholm, A., Wahlström, N., Lindsten, F. & Schön, Thomas B.: Machine Learning - A First Course for Engineers and Scientists. Cambridge University Press, 2021.
    Bohr, A. & Memarzadeh, K.: Artificial Intelligence in Healthcare. Academic Press, 2020. ISBN 9780128184387.
    Xing, L., Giger, Maryellen L. & Min, J.: Artificial Intelligence in Medicine: Technical Basis and Clinical Applications. Academic Press, 2020. ISBN 9780128212592.
Further Information
Course code
  • BMEN35F
Administrative Information
  • 2022-03-24
  • Professor Thomas Johansson

All Published Course Occasions for the Course Syllabus

1 course occasion.

Start Date End Date Published

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