Benefits and pitfalls of AI in radiology

Benefits and pitfalls of AI in radiology unknown

After his uncle died in 2018 from late-stage lung cancer, Pelu Tran determined that artificial intelligence could have made a difference, potentially aiding doctors in finding the cancer earlier on imaging scans and avoiding the delay of critical treatment.

Tran, an entrepreneur who studied both medicine and engineering at Stanford University, found that there were eight different companies with AI applications cleared by the Food and Drug Administration that might have discovered his uncle’s lung cancer if they had been used.

His desire to prevent future missed diagnoses and protect patients from medical error informed the mission of the technology company he co-founded, Ferrum Health, which helps health systems deploy AI.

“Most diagnostic decisions today are made without the help of any sort of artificial intelligence, and that's something we realized just had to change,” Tran said.

AI is used more often in radiology than any other specialty, with uses ranging from scheduling appointments and triage exams to diagnosing using imaging technology. In some instances, AI can be used to improve the acquisition of images, allowing MRI scans to go faster and capture higher quality, clearer images, which saves money and is easier for the patient. Machine learning technology is often touted as a way to save radiologists’ time and improve diagnostic quality, though some argue AI is not worth the investment amid concerns of potential inaccuracy and bias.

Read the complete story at our sister publication, Modern Healthcare