With the advent of AI/ML algorithms in healthcare there are huge opportunities to advance outcomes for patients across a broad spectrum medical conditions.
These days, electronically-stored medical imaging data is plentiful and AI algorithms can be fed with this kind of dataset, to detect and discover patterns and anomalies. Machines and algorithms can interpret the imaging data much like a highly trained radiologist could — identifying suspicious spots on the skin, lesions, tumors, and brain bleeds. The usage of AI/ML tools/platforms for assisting radiologists is, therefore, primed to expand exponentially.
This approach solves a critical problem in the healthcare domain because, throughout the world, well-trained radiologists are becoming hard to come by. In most circumstances, such skilled workers are under enormous strain due to the deluge of digital medical data. An average radiologist needs to produce interpretation results for one image every 3–4 seconds to meet the demand.
However, in today’s healthcare system access to quality healthcare data like radiology diagnostic images (DICOM) is controlled by large healthcare providers (Hospital/Health Systems) and governed by lots of data privacy laws that make working with this data very challenging including Health Insurance Portability and Accountability Act (HIPPA).
Leveraging the NMC secure storage and computing capabilities I envision a platform whereby large healthcare providers or individuals could safely upload their DICOM images to anonoumously facilitate the data needs for AI algorithms looking to advance care. This system would eliminate the challenges of HIPPA compliance concerns.