CopilotCAD: Empowering Radiologists with Report Completion Models and
  Quantitative Evidence from Medical Image Foundation Models
Sheng Wang Tianming Du Katherine Fischer Gregory E Tasian Justin Ziemba Joanie M Garratt Hersh Sagreiya Yong Fan
Computer-aided diagnosis systems hold great promise to aid radiologists and
clinicians in radiological clinical practice and enhance diagnostic accuracy
and efficiency. However, the conventional systems primarily focus on delivering
diagnostic results through text report generation or medical image
classification, positioning them as standalone decision-makers rather than
helpers and ignoring radiologists' expertise. This study introduces an
innovative paradigm to create an assistive co-pilot system for empowering
radiologists by leveraging Large Language Models (LLMs) and medical image
analysis tools. Specifically, we develop a collaborative framework to integrate
LLMs and quantitative medical image analysis results generated by foundation
models with radiologists in the loop, achieving efficient and safe generation
of radiology reports and effective utilization of computational power of AI and
the expertise of medical professionals. This approach empowers radiologists to
generate more precise and detailed diagnostic reports, enhancing patient
outcomes while reducing the burnout of clinicians. Our methodology underscores
the potential of AI as a supportive tool in medical diagnostics, promoting a
harmonious integration of technology and human expertise to advance the field
of radiology.