
In a recent interview with Diagnostic Imaging, Eliad Josephson discussed key attributes of the HLTH USA conference, AI trends and an ongoing shift of emphasis on the development of predictive technologies with diagnostics at center stage.

In a recent interview with Diagnostic Imaging, Eliad Josephson discussed key attributes of the HLTH USA conference, AI trends and an ongoing shift of emphasis on the development of predictive technologies with diagnostics at center stage.

In a recent interview, Phillip Koo, MD, discussed emerging trends with AI, theranostics and empowered patients, and how these developments may lead to challenges and potential opportunities for radiologists and nuclear medicine physicians.

How will generative AI software be evaluated by the FDA in the future? What’s next for point-of-care ultrasound? What steps have been successful in facilitating adoption of AI applications across enterprise systems? Leaders in radiology and health care will be discussing these topics and more at the upcoming HLTH USA conference.

In the third of a three-part podcast episode, Wendie Berg, MD, PhD, FACR, FSBI, Manisha Bahl, MD, and Amy Patel, MD discuss recent developments with autonomous AI in breast imaging and share their perspective on the overall potential of AI to help mitigate the radiologist shortage.

In the second of a three-part podcast episode, Wendie Berg, MD, PhD, FACR, FSBI, Manisha Bahl, MD, and Amy Patel, MD discuss how to handle disagreements with adjunctive AI in breast imaging, whether to include AI assessments in PACS, and whether patients should pay for the use of adjunctive AI.

In the second of a two-part podcast episode, Lauren Nicola, MD, Christoph Wald, MD, PhD, MBA, and Peter Shen discuss challenges with evaluating value and ROI for AI software, the physician time required to do so, and possible solutions to facilitate improved clarity and consistency with AI reimbursement.

In the first of a two-part podcast episode, Lauren Nicola, MD, Christoph Wald, MD, PhD, MBA, and Peter Shen discuss current challenges with reimbursement for AI in radiology.