By Matthew Brady 
A paper published in the Journal of Biomedical Sciences describes how DeepSeek, along with its novel underlying technology and training framework, could define the blueprint for the next era of AI in healthcare.
By making its reasoning processes more transparent and comprehensible, the authors argue, DeepSeek could offer a new approach to clinical decision-making—supporting healthcare professionals in trusting and validating its recommendations.
However, they also caution that verbose responses may pose challenges in clinical contexts. For instance, in the task of clinical note generation, excessively detailed outputs that include reasoning steps may overwhelm clinicians, who are responsible for reviewing and approving these notes. This could, in turn, increase the risk of critical details being overlooked.
While DeepSeek’s new reasoning-focused training framework may offer valuable insights, the authors stress the importance of rigorously testing these capabilities in real-world clinical settings.
As DeepSeek emerges as a focal point in the development of large language models for healthcare, the authors highlight the growing need for a collaborative, co-design approach to guide their integration into clinical practice.
Such an approach should involve a broad range of stakeholders—including technology developers, clinicians, ethicists, domain experts, payers, policymakers, and end-users—to ensure that these systems address real-world needs and are developed with ethical and practical considerations in mind.
