Evagoras Xydas, IREROBOT; Or Shaked, Briya; Kelly H. Zou, AI4Purpose
Artificial intelligence is rapidly transforming healthcare, and the webinar “Artificial Intelligence (AI) in Healthcare,” presented by Or Shaked, Evagoras Xydas, and Kelly H. Zou, provided a look at this transformation unfolding across research, clinical practice, and the broader health workforce.
As part of the AI‑Era Career Journeys series, the webinar brought together experts from robotics, clinical research, and responsible AI strategy to help participants understand not only what AI can do today, but also how it’s reshaping skills and mindsets of future healthcare careers. The event attracted graduate students, early‑career researchers, clinicians, and professionals from diverse backgrounds who wanted to understand how AI is influencing their fields and how they can prepare for the changes ahead.
Xydas opened the webinar with a discussion about the growing role of AI‑powered robotics in healthcare. Robotics and haptics have long been associated with surgical and disability assistance, respectively. Xydas emphasized how the field has expanded far beyond the operating room into daily living. AI now enables robots to interpret complex environments, adapt to patient needs, and collaborate more naturally with human clinicians. These systems are being deployed in rehabilitation centers, hospital logistics, infection control, and patient companionship scenarios. Xydas highlighted how AI‑enhanced surgical robots improve precision and reduce complications. But equally important are the less glamorous robotic systems that support nurses and technicians by handling repetitive or physically demanding tasks.
This shift isn’t about replacing healthcare workers. It’s about augmenting their capabilities and reducing burnout. Robots that can lift patients, deliver supplies, or monitor vital signs free up clinicians to focus on the human‑centered aspects of care that machines cannot replicate. Xydas stressed that the future of healthcare will involve hybrid teams of humans and intelligent machines working side-by-side, each contributing their strengths.
Led by Shaked, the conversation then moved into clinical research, where AI is accelerating discovery and reshaping how evidence is generated through its AIRE agentic AI system for generating real-world evidence.
Shaked explained that healthcare research is becoming increasingly data‑rich, with information coming from electronic health records, imaging, genomics, wearables, and patient‑reported outcomes. Traditional analytical methods struggle to keep up with this complexity, but AI excels at identifying patterns, predicting outcomes, and synthesizing large datasets.
Shaked described how machine learning models can help design more efficient clinical trials by predicting recruitment challenges, identifying optimal patient subgroups, and simulating potential outcomes before a trial begins. Natural language processing is transforming the analysis of unstructured clinical notes, enabling researchers to extract insights that were previously buried in text. AI is also playing a growing role in real‑world evidence generation, helping researchers validate findings outside of controlled trial environments.
Shaked emphasized that AI is not replacing scientific judgment, but enhancing it, allowing researchers to ask better questions and explore hypotheses that would have been impossible to test manually.
Speakers stressed that professionals don’t need to become data scientists, but they do need to be comfortable working in environments in which AI is integrated into everyday workflows.”
The third speaker, Zou, focused on the risks and challenges associated with deploying AI for patient-centric digital health in the forms of smart sensors, devices, and apps. While AI offers enormous potential, it also raises concerns about bias, transparency, data privacy, and accountability. Zou explained that AI systems trained on biased datasets can inadvertently perpetuate or amplify inequities in healthcare. For example, if an algorithm is trained primarily on data from one demographic group, it may perform poorly for others. Ensuring fairness requires careful dataset curation, ongoing monitoring, and a commitment to inclusive design.
Transparency is another critical issue. Clinicians need to understand how AI systems arrive at their recommendations, especially when those recommendations influence diagnoses or treatment decisions. Black‑box models may be powerful, but they can undermine trust if their reasoning is opaque. Zou argued that explainability should be a core requirement for any AI system used in clinical care.
A recurring theme throughout the webinar was the importance of responsible and ethical AI implementation. Throughout all talks, data privacy and security were central to the discussion. Healthcare data is highly sensitive, and AI systems often require large amounts of it. Speakers emphasized the need for robust safeguards, clear consent processes, and strict governance frameworks to ensure patient information is protected.
The webinar speakers highlighted that responsible AI isn’t just a technical challenge—it’s a cultural one. Organizations must foster environments where ethical considerations are prioritized, and clinicians must be empowered to question and challenge AI outputs when necessary. Speakers encouraged participants to view responsible AI as an ongoing process, rather than a one‑time compliance task.
Speakers also emphasized that future workforces will need a blend of clinical expertise, data literacy, and technological fluency. Participants were encouraged to develop skills in interpreting AI‑generated insights, collaborating with digital tools, and understanding the limitations of machine-learning models. Speakers stressed that professionals don’t need to become data scientists, but they do need to be comfortable working in environments in which AI is integrated into everyday workflows. Curiosity, adaptability, and a willingness to engage with new technologies will be essential traits for success.
The event concluded with a Q&A session, where the remaining 50 participants were asked to offer their real-world pain points in the AI‑healthcare space, strategies for staying current with rapid advances, and ways to balance innovation with ethical responsibility. Speakers encouraged attendees to seek interdisciplinary collaborations, engage with open‑source tools, and participate in professional communities focused on AI and healthcare. They also emphasized the importance of maintaining a patient‑centered perspective, reminding participants that technology should always serve humans.
Overall, the webinar explored how AI is reshaping healthcare, offering participants the benefits and risks of an AI‑driven future. The session made clear that amid rapid evolution, the most important factor will be the people who design, implement, and use AI. By fostering a workforce that is informed, ethical, and adaptable, the healthcare community can harness AI to improve outcomes, enhance efficiency, and create a more equitable and resilient system.
Editor’s Note: Views expressed by professionals in this article may not necessarily reflect those of their respective organizations. This webinar was sponsored by the American Statistical Association’s Caucus of Industry Representatives and New York City Chapter, AcademyHealth’s Health Information Technology (HIT) Interest Group, and the AI4Purpose Network.
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