Was: Medical AI
Wo: Schlieren (ZH)
Von: Do, 19.10.2023 09:00
Bis: Fr, 20.10.2023 17:00

Medical AI

Medical AI: How to successfully build compliant solutions

Acquire all the necessary steps to develop Machine Learning systems according to the current regulatory guidelines for Medical AI. Build solid and safe AI products following a methodology that is useful in all domains, including outside the medical sector. Use the right framework to achieve regulatory approval for your products.

Subjects that will be discussed:
Building a framework to develop regulated AI
How to shape your project from a risk management perspective
Necessary considerations with regards to data quality and ethics
How to conduct a proper validation of both models and system

Course duration
2 days

Course overview

Medical AI is an exciting space with great opportunities to advance medicine. However, the rapidly evolving regulatory landscape can be daunting for teams looking to develop Medical AI solutions. In this course, you will learn the process of developing AI in a regulated environment, from the proof-of-concept stage to operationalisation.

We focus on the concrete steps necessary to comply with the FDA and EU market regulations (MDR, ISO 14971, IEC 62304). The theory will be mixed with hands-on exercises. The half-day workshop with a concrete ML example at the end of the course will solidify the knowledge.

Course objectives

This course enables you to establish a concrete framework for developing regulated machine learning solutions while feeling confident with the concepts of risk management, good Machine Learning practice and how to conduct a solid validation and documentation.

Target groups

Startups, pharma and medtech, especially roles such as data scientists, biostatisticians, ML specialists, project managers, and quality managers as well as anyone with a foundation in Machine Learning who is looking to gain a better understanding of how to develop Medical AI solutions. Participants need to have a basic understanding of Machine Learning. Knowledge in SW engineering/coding is not necessary.

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