This isn't a protip. Friends discuss things, even intimate things.

I develop ML for medical devices. The integrated AI systems you are imagining are unlikely to be adopted for the foreseeable future.

First, the software in healthcare cannot be centralized. Every point of care has a LIMS (Laboratory Information Management System) for digitally managing lab results. Installing a modern diagnostic instrument involves communication with the LIMS. The problem is that virtually every clinic's LIMS is a bespoke creation by their IT staff. There exist almost no standards for the form of data in these systems. Performing a LIMS integration at one site does not make the process any easier for the next site. Thus an integrated AI solution for a clinic would need to be tailored to that site. There are very few sites that would generate enough data on their own to train a modern ML solution.

Similarly, the number and types of diagnostic tests performed are very different between sites. Further, there are often dozens of commercial options for any given test. So two identical patients at different sites will have different lab tests performed, and those tests may have slightly different results/coverage based on the technology adopted by that lab.

While this may seem messy, it actually makes sense for the field. Healthcare needs vary widely among different geographic contexts. Hospital-acquired infections tend to be unique to specific sites. Common injuries/illnesses/etc also tend to vary with urban vs rural environments, and the local weather patterns and ecology.

For some types of healthcare where geography is not so important, specialized centers will meet much of the demand. There will be trauma centers and cancer centers that treat similar ailments for a large geographic area. Those centers will be the best places to develop integrated AI solutions, but those solutions will only work for other similar large centers.

Additionally, the regulatory and IP environment in healthcare is not conducive to integrated solutions. Diagnostic IP is fragmented across thousands of companies, and none of them will voluntarily cooperate to help develop standards for integration. Some large companies are marketing integrated solutions, but these function as whole-sale replacements for specific lab workflows. Very few clinics will have the funding required to replace their existing workflow all at once, and even these integrated workflows require extensive customization in capability tailored to each site's needs. In the US, an integrated solution must go through the same regulatory process as the standalone tests, even if those tests are already approved by the FDA. This effectively doubles the costs of development.

COPAN is one company that has done great work in AI-assisted workflows through their integrated microbiology solutions. Despite this, they have less than 1000 sites deploying their solutions. This is because they rely on older methods and tests for integration. The newer/faster technologies are owned by other companies, requiring a partnership for integration.

Currently, AI in diagnostics is limited to what one company can accomplish, and even then the algorithms must be frozen. Updating a model based on new data requires another round of clinical trials for FDA approval. Data acquired at clinical sites cannot be included in these updates due to privacy laws. Even user telemetry data is nearly impossible to extract from a field instrument due to IT security practices.

The cancer cell needs to be circulating and viable for this assay to work. They isolate individual cells from the blood and culture them to determine if any are cancerous. This is not a sensitive assay; far from it. They can shift through a little over 10,000 cells per assay. But blood has over 10 billion blood cells per ml. The cancer cells would need to be at level greater than 1 million per ml to be detected reliably.