[Press Release] SmartLab Introduces SmartPath: An AI Pathology Platform Transforming End-to-End Cancer Care

SmartPath AI Pathology Platform Banner

SmartLab at The Hong Kong University of Science and Technology (HKUST) today launched SmartPath, a comprehensive artificial intelligence (AI) system designed to transform the entire pathology workflow for cancer care. Led by Assistant Professor Hao Chen, Director of the Collaboration Center for Medical and Engineering Innovation, SmartPath provides integrated support for clinical diagnosis, subtyping, biomarker quantification, treatment response assessment, and prognostic follow-up across a wide spectrum of cancers—accelerating turnaround times and enhancing personalized treatment.

Groundbreaking Features for End-to-End Clinical Support

Developed from one of the largest and most diverse pathology datasets—over 500,000 whole-slide images spanning 34 major tissue sites—SmartPath assists healthcare professionals with 100+ clinical tasks, including cancer classification, subtyping, treatment response evaluation, survival prediction, and automated pathology report generation.

SmartPath is powered by two integrated large AI models:

  • Generalizable Pathology Foundation Model (GPFM)
    A unified framework for accurate tumor identification, subtyping, and biomarker quantification across diverse tissues. GPFM supports survival outcome prediction and treatment response assessment, forming a data-driven foundation for personalized therapy.
    Sources: arXiv (Towards a generalizable pathology foundation model via unified knowledge distillation, 2024), HKUST Smart Lab publications.

  • Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model (mSTAR)
    Fuses whole-slide pathology images with contextual data (pathology reports and transcriptomics) to enable minute-level automated report generation and powerful visual question-answering on slide regions.
    Sources: arXiv (A multimodal knowledge-enhanced whole-slide pathology foundation model, 2024), Smart Lab technical page.

Engineered for seamless clinical integration, SmartPath streamlines the cancer care cycle—from rapid slide analysis and proactive risk alerts to AI-assisted reporting—reducing diagnostic bottlenecks and enabling pathologists to focus on complex decisions.

SmartPath Features and Workflow

Proven Performance in Rigorous Clinical Trials

SmartPath is undergoing multi-center prospective validation with top-tier hospitals in Hong Kong and the Chinese Mainland. In comprehensive benchmarking, it significantly outperformed existing models. A recent prospective study at Nanfang Hospital reported accuracy >95% across multiple cancers (including lung, breast, and colorectal), confirming SmartPath’s ability to enhance diagnostic accuracy, reliably predict patient survival, and rapidly generate detailed pathology reports.
Source: HKUST official news release; Medical Xpress; The Standard.

Clinical Prospective Validation at Partner Hospitals

Leadership Voices

Prof. Hao Chen, Director of the Collaboration Center for Medical and Engineering Innovation and Assistant Professor in the Departments of Computer Science and Engineering and Chemical and Biological Engineering, said:

“SmartPath has been built and validated with a strong network of clinical partners. Across a wide array of real-world clinical tasks, the system consistently ranks first in benchmarking—especially in malignancy identification and treatment response prediction. With continuous real-world feedback, SmartPath keeps learning and improving, setting a new standard for intelligent, personalized medicine.”

Prof. Liang Li, Director of the Department of Pathology at Nanfang Hospital and Professor at Southern Medical University, commented:

“Preliminary results from our prospective trials are highly encouraging. SmartPath improves malignancy identification, provides reliable prognostic predictions, and significantly shortens diagnostic turnaround time through rapid generation of preliminary reports—crucial for time-sensitive cancer cases. This is the future of pathology, where AI augments precision and empowers data-driven clinical decisions.”

Prof. Hao Chen and Prof. Liang Li at SmartPath Launch

Real-World Impact and Collaboration

SmartPath is being deployed with over a dozen leading hospitals across Hong Kong and the Chinese Mainland, enabling robust validation across diverse patient populations and clinical tasks. The benchmark and framework established by HKUST and its partners set a new standard for computational pathology in precision oncology and smart healthcare worldwide. Ongoing research is expanding SmartPath to additional cancer types—including rare and genetically complex malignancies—to further enhance predictive models and patient stratification.

HKUST SmartPath Research Team and Clinical Partners


About Smart Lab

Smart Lab, led by Prof. Hao Chen at HKUST, is dedicated to advancing trustworthy AI technologies for healthcare and science. Our research spans large-scale models for healthcare, computer-assisted intervention, AI for science, and bioinformatics. Our mission is to drive a transformative revolution in medical practice and scientific discovery, shaping a healthier future.

About The Hong Kong University of Science and Technology

The Hong Kong University of Science and Technology (HKUST) is a world-class university that excels in innovative education, research excellence, and impactful knowledge transfer. With a holistic and interdisciplinary approach, HKUST was ranked 3rd in THE’s Young University Rankings 2024, and 19th worldwide and No.1 in Hong Kong in THE Impact Rankings 2025. Thirteen HKUST subjects were ranked among the world’s top 50 in the QS World University Rankings by Subject 2025, with Data Science and Artificial Intelligence ranked 17th globally and first in Hong Kong. Over 80% of HKUST research was rated “internationally excellent” or “world leading” in Hong Kong’s latest Research Assessment Exercise. As of July 2025, HKUST members have founded over 1,900 active start-ups, including 10 unicorns and 17 exits (IPO or M&A).

Sources and Further Reading