WORCESTER MEDICINE
music, may promote anticipation and expectation – two key factors that drive motivation which have been described as fundamental for the musical experience to understand the effects of music on emotion [6] – towards increasing engagement with digital interface. Data from one’s behavior with the digital interface triggered by memorable music may inform purposeful design goals for serious games that may contribute towards enhancing older adults’ cognitive abilities. A potential integral of artificial intelligence may involve assigning a novel dimension to its existing problem-solving field by adapting to varying states of cognitive function for monitoring purposes based on an individual’s interaction with a musical digital interface. In summary, achieving digital inclusion of elderly people must look beyond increasing access to technology. It must actively question the meaning of social inclusion that stems from rigorous digital interface design research that evokes purpose for the elderly’s fundamental wellbeing as coexisting citizens. + Sunny Choi, PhD is the vice president of product and operations at Clefer. She has worked on building education software applications for over 5 years. Email: choi.sunnys@gmail.com References 1. Olsson, T., & Viscovi, D. (2020). Who actually becomes a silver surfer? Prerequisites for digital inclusion. Javnost-The Public, 27(3), 230–246. 2. World Economic Forum. How can we ensure digital inclusion for older adults? (2022, December 12). https://www.weforum.org 3. Wiemeyer, J., & Kliem, A. (2012). Serious games in prevention and rehabilitation—a new panacea for elderly people?. European Review of Aging and Physical Activity, 9(1), 41–50. 4. Mitgutsch, K., & Alvarado, N. (2012, May). Purposeful by design? A serious game design assessment framework. In Proceedings of the International Conference on the foundations of digital games (pp. 121–128). 5. Worschech, F., James, C. E., Jünemann, K., Sinke, C., Krüger, T. H. C., Scholz, D. S., Kliegel, M., Marie, D., & Altenmüller, E. (2023). Fine motor control improves in older adults after 1 year of piano lessons: Analysis of individual development and its coupling with cognition and brain 2 structure. The European journal of neuroscience, 57(12), 2040–2061. https:// doi.org/10.1111/ejn.16031 Click here for additional reference(s).
A.I. In Medicine
Pixels, Patterns, and Patients: Radiology Residency in the AI Revolution Michael J. Purcaro, MD/PhD
A
s the Industrial Revolution once replaced the rhythmic trots of horses with the rhythmic hums of machines, artificial intelligence (AI) is replacing the manual intricacies of medicine with algorithms that promise to reshape our understanding of health and disease. Cutting across disciplines and industries, AI is not merely an evolution; it’s a revolution, changing the very foundation upon which systems operate. Medicine, always one of the first consumers of new technology, is itself on the precipice of revolution brought by AI. Radiology, perhaps more than any other field rooted in technology and innovation, is at the epicenter of this seismic shift. The potential of AI in enhancing diagnosis, treatment, and overall patient care is immense. But like any powerful tool, its true value can only be harnessed when understood in depth. As the adoption of deep learning tools in diagnostic imaging surges, the subtleties and potential errors of AI underscore the need for radiologists who excel not only in diagnostic acumen but also in liaising with computer scientists and software engineers. It becomes crucial, then, for radiology residents—future stalwarts of the discipline— to delve deep into the intricacies, challenges, and promises of AI. Integrating AI into the radiology resident education curriculum is an exciting but challenging new endeavor. A study led by Emory University in early 2023 revealed that 83% of surveyed radiology residents across 21 U.S. residency programs desired the inclusion of AI and machine learning education in their curriculum; less than 20%, though, had actually received any formal AI education or research experience (1). At the University of Massachusetts, our residency program has found several ways to integrate AI into our training. We have a wide variety of conferences from AI radiology subject matter experts, as well as didactic sessions, online and in-person AI conferences, and journal clubs to help navigate through the dilemmas and intricacies of AI. These sessions serve as dedicated spaces for exploring not only the mechanics of machine learning but also the ethical and professional conundrums that AI introduces to the field. The discussions facilitated by this forum enable residents to build a multidimensional understanding of AI, incorporating technical knowledge with ethics. Going beyond theory, the radiology department has integrated AI in practice. There are multiple AI tools being trialed by the attending radiologists. One tool in particular, Aidoc, has been integrated into the clinical process for multiple disciplines. Aidoc (AI-doc) is a sophisticated deeplearning convolutional neural network tool (2), currently used predominantly for annotating acute pathologies, including pulmonary embolisms and intracranial hemorrhages. Having processed tens of thousands of studies, the tool’s prowess in pulmonary embolism detection has reached unparalleled precision. If Aidoc identifies a potential embolism not mentioned in a radiology report, the system immediately flags the discrepancy for a thorough review. Aidoc’s capability to scan any CT study encompassing parts of the lungs has led to the serendipitous discovery of multiple pulmonary embolisms— incidents that would typically fly under the radar in conventional reviews. Senior residents, equipped with access to Aidoc and its suite of algorithms, witness firsthand the algorithm’s remarkable efficacy and its nuanced
Winter 2023
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