Miao Cui, M.D.

Biography

Miao Cui, M.D., M.S., is an Assistant Professor of Pathology at UT Southwestern Medical Center with subspecialty expertise in thoracic and breast surgical pathology and a particular academic interest in the integration of artificial intelligence, computational pathology, and quantitative methods into diagnostic practice.

Dr. Cui completed residency training in Anatomic and Clinical Pathology at the Icahn School of Medicine at Mount Sinai, where Dr. Cui served as Chief Resident, followed by consecutive fellowships in Oncologic Surgical Pathology and Thoracic Pathology at Memorial Sloan Kettering Cancer Center. Dr. Cui’s principal clinical and academic interests include neoplastic lung pathology, thymic lesions, mesothelioma, and interstitial lung disease, with an additional interest in breast pathology.

A major focus of Dr. Cui’s academic work is artificial intelligence and computational pathology. Dr. Cui has studied machine-learning approaches for pathology image recognition and classification and has designed and fine-tuned multiple domain-specific AI models intended for practical applications in pathology. These projects span histopathologic image interpretation, differential diagnosis, thoracic pathology and radiology integration, medical knowledge retrieval, scientific writing, and transformation of unstructured medical information into structured data for downstream analysis. Dr. Cui has also explored strategies to improve the reliability of large language models in medicine, including approaches designed to reduce data hallucination, which are the subject of a pending U.S. patent. This work is complemented by invited lectures on GPT-driven AI, language-model fine-tuning, and their applications in pathology informatics and daily practice.

Dr. Cui also has a longstanding interest in statistics, quantitative analysis, and data-driven biomedical research. Prior research has encompassed high-throughput proteomics, molecular profiling, signaling-pathway analysis, biomarker discovery, and computational pathology. This quantitative perspective increasingly informs Dr. Cui’s work in machine learning, where careful data curation, model validation, statistical interpretation, and clinically meaningful performance assessment are essential for translating computational tools into pathology practice.

Through the convergence of subspecialty diagnostic pathology, artificial intelligence, and quantitative methodology, Dr. Cui’s academic goal is to develop practical and reliable computational tools that augment pathologists’ expertise, improve diagnostic precision, and advance personalized patient care.