Getting genuinely useful new technologies, from wearables to clinical decision support, into the clinic has proven to be surprisingly challenging. Tanzeem K. Choudhury, PhD, of Cornell Tech joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss how to take research into the real world in a way that is scalable and affordable. Related Content:

• How Health and Technology Sectors Can Collaborate on Better AI-Assisted Wearables

What hard lessons did the US Department of Veterans Affairs (VA) learn in 3 decades of EHR development, and what can it teach us about building better AI in medicine? Stephen Fihn, MD, MPH, of the University of Washington, and formerly a leader in the VA's EHR efforts, joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss his experience at the VA building decision support tools and other technology. Related Content:

• The VA Was an Early Adopter of Artificial Intelligence to Improve Care—Here's What They Learned
• Hello, World—Introducing JAMA+ AI

Can data from wearable devices and electronic health records enhance quality improvement in health care? In this Q&A, Sachin Kheterpal, MD, MBA, the University of Michigan Medical School Associate Dean for Research Information Technology, joins JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss the potential of data from mobile devices to revolutionize patient care. Related Content:

• Tapping AI's Strengths—From Operating Room Safety to Wearable Device Interpretation

Can AI augment radiological processes, imaging analysis, and diagnosis? In this Q&A, Saurabh Jha, MBBS, MRCS, MS, an associate professor of radiology at the University of Pennsylvania, joins JAMA's Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss how AI could play a crucial role in improving access to medical imaging in remote, high-altitude, and low-income areas. Related Content:

• How Artificial Intelligence Will Enhance Imaging Access and Analysis
• Algorithms at the Gate—Radiology's AI Adoption Dilemma

Can AI/machine learning-driven digital phenotyping facilitate global personalized medicine? In this Q&A, Vikram Patel, MBBS, PhD, the Paul Farmer Professor and chair of the Department of Global Health and Social Medicine at Harvard Medical School, joins JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss how AI can enhance assessment and treatment solutions across lower-income nations. Related Content:

• One Day, AI Could Mean Better Mental Health for All

Could generative AI assist in extending access to vulnerable populations and begin to bridge the gap in disparities? In this Q&A, Davey Smith, MD, MAS, an infectious disease specialist and virologist at the University of California, San Diego, joins JAMA's Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to explore the implications of large language models for improving patient outcomes. Related Content:

• Will Generative AI Tools Improve Access to Reliable Health Information?

AI can understand brain signals linked to the sensory and motor processes involved in speech. In this Q&A, Edward Chang, MD, the chair and professor of neurosurgery at UCSF joins JAMA's Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss how AI has the potential to facilitate communication and how close AI development is to being able to translate human emotion. Related Content:

• Digital Avatars and Personalized Voices—How AI Is Helping to Restore Speech to Patients

Can AI enhance the speed and efficiency of interpreting ultrasounds and echocardiograms, thereby minimizing diagnostic errors? In this Q&A, Rima Arnaout, MD, a cardiologist and associate professor of medicine at the University of California-San Francisco, joins JAMA's Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss the transformative impact of AI on cardiac imaging. Related Content:

• How Machine Learning Might Help Improve Cardiac Imaging

Automation bias and shortcuts in clinical AI models have posed significant challenges. In this Q&A, Jenna Wiens, PhD, an associate professor of Computer Science and Engineering at the University of Michigan in Ann Arbor, joins JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss how we can start leveraging human collaboration with AI to provide more effective health care. Related Content:

• Blind Spots, Shortcuts, and Automation Bias—Researchers Are Aiming to Improve AI Clinical Models

AI in clinical practice needs ethical frameworks to avert future biases. In this Q&A, Marzyeh Ghassemi, PhD, the Herman L. F. von Helmholtz Career Development Professor at MIT in Electrical Engineering and Computer Science (EECS), joins JAMA's Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, to discuss ethical machine learning and responsible clinical implementation. Related Content:

• AI Developers Should Understand the Risks of Deploying Their Clinical Tools, MIT Expert Says