Artificial Intelligence / Health / Research
Challenges for the adoption of AI in the medical imaging clinic: Costs, explainable, non biased AI and consumer consent/engagement
There are numerous products and tools in the marketplace for artificial intelligence (AI) in medical imaging. This has spawned from many AI start-ups and companies.
How does one choose which product is suitable for use in their clinical case?
Should you do it now? Should you do it later?
There will be a discussion on what has been done at Alfred and Monash.
Will AI become less expensive?
What does it mean for the companies which have install base enterprise imaging and now enterprise AI platforms with API integration/plug ins for home grown and 3rd party AI tools/products?
What are the costs involved in running a medium to large medical imaging department? From equipment to infrastructure, to staff and service agreements.
What are the costs of AI products?
What is the return on this investment and how does it improve costs, workflow, quality of care, and patient safety in a radiology department?
How should we engage the consumers/patients?
Do we need to tell them we are using AI to help with their diagnosis and clinical care?
Do we need patient consent to use AI in their care?
What are the issues with using patient data to develop AI products and do we need consent if we develop products that becomes commercial products? Essentially monetizing a patient’s digital health data. Are patients entitled to a share of the commercial products’ revenue?
We may discuss data brokers, consent access to data, block chain consent. Most patients are happy with their digital health data being used for clinical care or even for research purposes, however it becomes a different discussion if their data is being used for commercial purposes.