Dive deeper into the aspects covered in this lesson with these readings that expand on the topics discussed. Each reading includes reflective questions to help you focus on key learning points and strengthen your understanding.

Reading: Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.
By Sebastian Vollmer.

You've just learned about the importance of reproducibility and the challenges associated with it. This reading helps you dive deeper by presenting 20 critical questions that researchers, healthcare professionals, and policymakers must address to ensure AI technologies are developed and applied in ways that are transparent, ethical, and effective. Challenge yourself to think deeply about how we can harness AI to truly benefit patients while mitigating the risks associated with its use.

Reading time: ~30 min

   Download the reading

Reflective questions

  • Which of the proposed questions would be most challenging to answer or address when designing a new study?
  • How crucial are data and code sharing for ensuring the reproducibility of a study?
  • What are the key aspects to focus on during AI software development to minimize potential disparities among patients?

Source: Vollmer S, Mateen B A, Bohner G, Király F J, Ghani R, Jonsson P et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness BMJ 2020; 368 :l6927 doi:10.1136/bmj.l6927.  Link