BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routine
arXiv:2606.04911v1 Announce Type: cross Abstract: Breast cancer remains a leading cause of cancer-related mortality among women. Its clinical management requires multimodal reasoning across a clinical workflow that spans \textit{screening}, \textit{diagnosis} and \textit{treatment planning}, where each stage involves distinct imaging modalities, task objectives, and reasoning patterns. However, constrained by data scarcity and model versatility, existing medical MLLMs are typically evaluated on isolated modalities or narrow task families, limiting their ability to support workflow-level clinic
The proliferation of Large Language Models (LLMs) and advanced AI in medical imaging is creating new opportunities to address complex healthcare challenges like breast cancer diagnosis and treatment.
A multimodal LLM capable of integrating screening, diagnosis, and treatment planning across various imaging modalities signifies a major step towards comprehensive AI-driven medical solutions.
Current fragmented medical AI applications are starting to consolidate into more holistic, workflow-spanning systems, promising more integrated patient care and potentially higher accuracy.
- · AI healthcare technology companies
- · Oncologists and radiologists
- · Breast cancer patients
- · Medical research institutions
- · Traditional, siloed medical software vendors
- · Companies specializing in single-modality AI solutions
- · Diagnostic processes with high human error rates
Improved early detection and personalized treatment plans for breast cancer patients due to AI-driven insights.
Accelerated development of similar multimodal AI systems for other complex diseases, broadening AI's application in medicine.
Potential for AI to redefine medical training, with a greater emphasis on interpreting AI outputs and managing AI systems rather than solely foundational knowledge acquisition.
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Read at arXiv cs.CL