From the October 2022 issue of HealthCare Business News magazine

As we drive toward more personalized diagnostic and treatment decisions, AI’s implicit bias is another area of concern. For example, a breast cancer algorithm can be trained on a large batch of patients from the Northeast. But patients across the country, much less the world, are hardly identical. Can vendors develop a breast cancer algorithm that is batch-trained using Northeastern patients and can also learn from new patient data as it is processed? This self-adjusting algorithm would be more complimentary to the patient population to which it is being applied — be it women from the South or South America.

The fact that radiologists are asking these questions, and providing feedback to vendors, is significant. Undoubtedly, this scrutinization of AI — and for that matter, its very adoption by healthcare institutions — would gain momentum if reimbursement incentives were tied to AI’s use in radiology. Reimbursement is likely forthcoming, but its form and arrival date are unclear. Lack of reimbursement must not weaken radiology’s collective resolve to improve AI and maximize its value.


AI has done much for medical imaging in the relatively short time that it has been commercially available. If radiologists and vendors address these questions, it will advance beyond its early promises of process automation and efficiency gains to enable more informed decision-making. AI will then become a more fully realized tool — one that is more fully embraced by the medical imaging community and, ultimately, more beneficial to the patient.

About the author: Peter Shen is the head of the digital & automation business at Siemens Healthineers North America.

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