Table 1

10 tips to get started with artificial intelligence healthcare projects

Phase 1: conceptualisation
1Build a collaborative science team
Recruit widely, select complementary expertise. Engage gatekeepers early.
2Engage frequently with the end user
What is the clinical problem to be addressed? End user engagement is a process, not an event. Develop digital transformation, not isolated algorithms.
3Build collaboration agreements early
Check for existing agreements. Use contracts and IG teams. Share your experiences with other CSTs.
4Ethics: present a balanced view
Present a balanced view of the challenges and benefits of AI projects. Seek out ethics review boards with prior experience. Ethical review may be protracted.
5Invest in data science training for healthcare professionals in your team
A common language is needed for the CST to work at its best.
Phase 2: data acquisition and preparation
6Do not underestimate the challenge of data extraction
Understand what data you really need. Identify a data champion. Engage healthcare informatics teams early; it will take longer than you expect. Consider building or contributing to a meta-data catalogue at your institution.
7Protect patients’ data
Give serious consideration to how you will anonymise and protect patient data from the outset of the project.
8Remember that healthcare data varies in quality and reliability
Healthcare data are influenced by a wide variety of factors. Clinicians can play a key role in interpreting and overcoming quality and reliability issues.
Phase 3: AI application
9Design AI that can be trusted with patient care
Use the right algorithms for your data and your end user. Strive for generalisable, well-validated solutions.
Phase 4: translation
10Be mindful of medical device regulation
Be mindful of the requirements for medical device regulation as your healthcare data science project progresses.
  • AI, artificial intelligence; CST, collaborative science team; IG, information governance.