Ep. 20 AI Takeover in Radiology: Unreasonable Fear or Reality with Dr. Suzie Bash

Neuroradiologist and AI champion Dr. Suzanne Bash and host Dr. Eric Gantwerker discuss the present and future applications of artificial intelligence (AI) in medical imaging. --- EARN CME Reflect on how this Podcast applies to your day-to-day and earn AMA PRA Category 1 CMEs: https://earnc.me/Cblsja --- SHOW NOTES In this episode, neuroradiologist and AI champion Dr. Suzanne Bash and host Dr. Eric Gantwerker discuss the present and future applications of artificial intelligence (AI) in medical imaging. Dr. Bash defines AI as a way to utilize computers to enhance human thinking. The ultimate goal is to use this technology to achieve better outcomes for clinical efficiency and quality of patient care. While AI technology has played a role in radiology for the past 15 years, its use has exploded in recent years. Dr. Bash describes her current role at RadNet, a large US-based outpatient imaging enterprise. She is interested in conducting clinical validation trials and evaluating product fit with her company's imaging facilities. Additionally, she serves as a clinical advisor to multiple AI companies. We cover a variety of AI applications in medical imaging, including triage, stroke detection, and cancer screenings. Dr. Bash gives us examples of companies and products that are at the forefront of each mission. She encourages all AI companies to stay in touch with clinicians to determine the clinical applicability of their products. One major factor to consider is how a product can be integrated with a radiologist’s workflow. Successful products will save the radiologist time, while adding value to their clinical decision-making. We also cover the challenges that the AI industry faces, such as FDA regulation and the question of legal liability when technology makes mistakes. Finally, Dr. Bash gives advice about entering the AI space, as well as the case for accepting and adapting to AI technology. She tells clinicians to find opportunities to learn from colleagues and experts and envision the types of products that will be useful in their particular clinical setting. --- RESOURCES RadNet: https://www.radnet.com/ Viz.ai: https://www.viz.ai/ Deep Medicine: https://www.amazon.com/Deep-Medicine-Artificial-Intelligence-Healthcare/dp/1541644638 Radiological Society of North America (RSNA): https://www.rsna.org/