Training the know-it-all intern
Medical Sieve is what IBM calls a “grand challenge project.” In contrast to product development, grand challenges are conceived In the tradition of pure science. They are long-term commitments — research for the sake of research — that have the potential to unlock new ways of improving the human condition.
At this stage, Medical Sieve is being developed with a focus on two high-value areas, radiology and cardiology, but the basic premise and the project development could eventually extend to other fields and functions.
Tanveer describes the challenge: “The comprehensive nature of it is that in the fields of radiology and cardiology there are over 40 modalities — we’re talking about CT, MRI, X-Ray, ultrasound — and then there are the diseases. In the field of cardiology alone there are over 600 diseases to cover. So, for a machine to be an assistant, it needs to have very strong capabilities on the knowledge side, the image interpretation side, and on the reasoning side — and the ability to summarize what is important to the clinician. All of this requires dedicated development of sophisticated algorithms in machine-learning-driven analytics and reasoning.”
In the fields of radiology and cardiology there are over 40 modalities… and over 600 diseases to cover.
It won’t be easy, and it won’t be fast, but the progress is steady and incredibly promising — and the medical community is getting on board. In June 2016, IBM announced the formation of the Watson Health medical imaging collaborative, a global initiative comprising leading health systems, academic medical centers, private radiology practices, ambulatory radiology providers, and imaging technology companies, all dedicated to making cognitive imaging an integral part of patient care. In February 2017, IBM debuted its first cognitive imaging offering, Watson Imaging Clinical Review, which is helping to reconcile discrepancies between patients' clinical diagnoses and administrative records.