Daniel Jesudason
Papers
1
Total Citations
2
H-Index
1
About
Daniel Jesudason is a surgical researcher whose work bridges the frontiers of artificial intelligence and clinical practice. His most-cited paper, “OpenAI's ‘Deep Research’ for the Generation of Comprehensive Referenced Medical Text: Uses and Cautions” (2025), has already garnered 2 citations, signaling early impact in a rapidly evolving field. Jesudason’s primary research areas include the application of large language models in medicine, surgical education, and the integration of AI tools into evidence-based surgical decision-making. His major contribution lies in critically evaluating the utility and limitations of AI-generated medical text, offering a cautious yet forward-looking framework for its responsible use in clinical and academic settings. This work is particularly notable for its relevance to the Australian and New Zealand surgical community, where Jesudason is recognized for fostering research productivity among both established and aspiring surgeons. By highlighting both the promise and pitfalls of AI in surgery, he has positioned himself at the forefront of a critical dialogue—one that will shape how future surgeons leverage technology to enhance patient care while maintaining rigorous scientific standards.
Research Focus
Key Achievements
Top Papers
- 1