Healthcare AI Beyond Image Analysis and the Radiology Reading Room

Artificial intelligence in healthcare first proved its impact in radiology, building on the breakthrough of AlexNet in natural image recognition and its rapid translation into medical imaging.

Today, however, the next frontier lies beyond image interpretation, and beyond radiology itself, toward applications that directly enhance clinical workflows, streamline patient pathways, and improve experience and outcomes.

A growing range of these applications, some already commercially available and widely used, are transforming care delivery end to end. These include patient-facing voice agents that improve access and experience, as well as multimodal models in areas such as oncology, integrating diverse data to generate diagnostic and prognostic insights beyond what human experts can achieve. In parallel, AI is supporting physicians through tools such as large language models, enabling augmented reporting, faster access to knowledge, reducing administrative burden, and freeing time for patient interaction.

These use cases are also the most likely to deliver tangible value to healthcare institutions, through efficiency gains, clearer return on investment, and a direct impact on patient management.

For healthcare organizations, the question is no longer whether to adopt AI, but which applications to prioritize, and how to fund, integrate, and deploy them safely and sustainably. The key challenge is separating signal from noise, and moving beyond prototypes and marketing claims toward operational reality, where performance, workflow integration, and clinical trust determine success.

This masterclass explores real-world AI applications beyond traditional image interpretation that are transforming healthcare in 2026, delivering both clinical and operational value across the care continuum. It will highlight the most promising and widely adopted use cases, and how organizations select, validate, and implement them in practice.

Attendees will gain practical insight into which applications are ready to scale, current adoption and barriers, and the levers to accelerate meaningful AI integration in healthcare.

Join us to discuss:

• What AI applications exist beyond traditional image interpretation in radiology, particularly in diagnostics and oncology, and which are the most promising

• Which applications are most mature in terms of clinical validation and readiness for real-world deployment

• What the main barriers to AI adoption are, and how organizations are addressing them