Dr. Dan Elton from Mass General Brigham discusses AI in healthcare, covering radiology, compliance, and medical record generation. They explore how foundational models will revolutionize AI in radiology and highlight promising use cases in healthcare such as automating billing codes and transcription processes.
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Quick takeaways
AI is transforming radiology by streamlining processes and improving efficiency in healthcare.
The slow adoption of technology in the medical field poses a challenge for implementing AI solutions.
Deep dives
Challenges of AI Adoption in Healthcare
The adoption of AI in healthcare is currently limited, with the majority of applications being used in radiology. Doctors are interested in using AI to streamline processes due to their heavy workload. However, the slow pace of technology adoption in the medical field is a major challenge in implementing AI solutions.
Pain Points in Radiology Workflow
Radiologists are overwhelmed with heavy workloads and time constraints, typically spending only 10 to 15 minutes on each study. The increasing complexity and volume of medical images make it difficult for radiologists to keep up. Another challenge is the transition to electronic health records, which has added more administrative tasks and increased the burden on doctors.
Promising AI Use Cases in Healthcare
While AI adoption in healthcare is still limited, there are promising use cases emerging. Triage systems based on AI are being widely used to quickly identify urgent cases, such as brain hemorrhages. Additionally, AI is being explored for tasks like automated medical billing code selection and medical transcription, which can significantly improve efficiency and reduce errors.
Today’s guest is Dr. Dan Elton, a Data Scientist at the Mass General Brigham Data Science Office. He’s also a fellow at the Foresight Institute and previously served as a staff scientist at the National Institutes of Health. He joins Emerj Senior Editor Matthew DeMello on today’s show to talk about promising AI use cases in healthcare, including use cases in radiology, compliance, and medical record generation, search, and summarization. Later, the two discuss how foundational models will transform how AI is used in radiology. To discover more AI use cases, best practice guides, white papers, frameworks, and more, join Emerj Plus at emerj.com/p1.
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