Time for AI to Deliver
COVID-19 Impact On Radiology
With the majority of non-urgent diagnostic procedures postponed in the early months of the pandemic, PACS are currently underutilized. Yet, despite the reduced volume of imaging studies at this time, radiology must remain ready for a spike in utilization. Once procedures return to their pre-pandemic or “new normal” schedules, the volume could jump on a moment’s notice. Imaging is expected to be there for patients, and 100% operational for when procedures come roaring back. Our focus at Dicom systems is to be the support mechanism for physicians and for medical imaging.
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The last portion of the webinar was dedicated to Q&A.
Can the algorithms, once implemented, learn from the patient population?
To date, the FDA has cleared or approved “locked” algorithms only. They define a “locked” algorithm as one that provides the same result each time the same input is applied to it and does not change. There is currently no approval for “learning” algorithms. These are described by the FDA as adaptive algorithms, for which the current regulatory frameworks were not designed. However, Dr. Chang, a panelist at a recent FDA workshop, shared that the FDA is now aware that continuous learning is one of the primary benefits of AI, and believes FDA approval for adaptive algorithms is not too far off.
Can AI be available to other disciplines, besides imaging? Can imaging and non-imaging data be combined in a deep learning algorithm?
While radiology has a head start in AI due to the long history of storing studies digitally, other disciplines, such as cardiology or pathology, can absolutely leverage AI. AI is also expected to give ophthalmologists new automated tools for diagnosing and treating ocular diseases, such as diabetic retinopathy (DR) and diabetic macular edema.
In fact, Florent Saint-Clair emphasized, AI must integrate all aspects of diagnostics in order to become more and more intelligent over time. The key to that level of integration is all vendors adhering to industry standards –DICOMweb Dicomweb and FHIR – to marry those results. In time, cross-domain standards will emerge and provide ways to integrate data from different sources and disciplines.
Can AI help thwart a pandemic by identifying animal viruses?
Scientists estimate that 1.67 million viruses exist on earth. A fraction of the 1M+ animal viruses have the potential to “jump species,” a process called zoonotic spillover. Whether or not a virus can make such a leap and cause the next pandemic depends on a complex combination of factors related to ecology, viral evolution, and human immunity.
Dr. Chang explained how AI applications could address this with “virtual experiments” in cases where we have a lot of historic data, but the cost of lab experiments is prohibitively high. An algorithm could be trained to predict which virus has the capacity to become a threat.
The COVID-19 pandemic has, without a question, upended healthcare worldwide. But not all changes it has brought about are disruptive or catastrophic. With increased funding, policy changes, and accelerated approvals, AI adoption in healthcare may become a silver lining of the pandemic.