How to deliver a successful AI project in Medicine – from concept to reproducible processes and solutions

Artificial intelligence is becoming increasingly critical to develop innovative, competitive and differentiated medical businesses and products. Unfortunately, AI projects may fail, due to lack of proper vision, guidance, and data strategy. Deployed AI solutions always contain a degree of human and societal biases that may influence the results. Such risks bear consequences for organizations in terms of financial loss, company reputation, and beyond.

On 27-04-2021, within the programme of Applied Machine Learning Days 2021, I have delivered a full-day workshop with the title “How to Deliver Successful AI Projects in Medicine”. The workshop was delivered online, bearing in mind the precautions against COVID. I had the privilege to collaborate with Pawel Rosikiewicz and Oksana Riba Grognuz, my co-associates from SwissAI.

Photo Credit: Pawel Rosikiewicz

The workshop addressed several considerations for successful AI projects, such as:

  • How to Innovate and differentiate through AI projects?
  • What are the main challenges in AI projects?
  • How to mitigate risks associated with AI?
  • How to estimate the required resources (timeline, budget, team size)?
  • Why do we need to address cognitive biases of users and how to do this?

In this workshop, we have also premiered the AI Readiness Assessment Matrix, a self-reflection tool intended for organizations who wish to start or continue implementing an AI system. With this tool, they can assess the level of readiness of their business, product, and data infrastructure for AI; and further identify the potential actions to improve their readiness (and hence the feasibility) of their AI project.

The AI Feasibility Assessment Canvas, reference: https://onuryuruten.com/ai-readiness-assessment/

Much of the content will be primarily available to AMLD participants; however, don’t hesitate to get in touch if you’re interested in similar content!

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