About

Richard E. Fan is a Clinical Assistant Professor in Urology at Stanford University and is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His role involves integrating artificial intelligence into medical imaging and healthcare, contributing to the center's mission to advance AI applications in medicine. His work focuses on leveraging AI technologies to improve diagnostic and treatment processes in urology, supporting innovative research and education in this interdisciplinary field.

Research topics

• Medicine
• Internal medicine
• Radiology
• Pathology
• Artificial Intelligence
• Computer Science
• Nuclear medicine

Selected publications

• 901 A Clinicopathologic Review of 111 Prostate Biopsies Following High-Intensity Focused Ultrasound (HIFU) Therapy

  Laboratory Investigation · 2026-03-01

  article
  PublisherDOI

• Prostate Cancer Detection on Micro-Ultrasound Raw Data Using a Deep Learning Neural Network

  Ultrasound in Medicine & Biology · 2026-05-01

  article
  PublisherDOI

• Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study

  Radiology Imaging Cancer · 2026-04-10

  articleOpen access

  A simulated artificial intelligence–driven triaging workflow for clinically significant prostate cancer diagnosis at MRI triaged 49% of examinations, improving specificity while maintaining radiologist-level sensitivity.

  PublisherDOI

• LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch

  arXiv (Cornell University) · 2025-01-13

  preprintOpen access

  We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360. While open-source LLMs continue to advance, the answer to "How are the largest LLMs trained?" remains unclear within the community. The implementation details for such high-capacity models are often protected due to business considerations associated with their high cost. This lack of transparency prevents LLM researchers from leveraging valuable insights from prior experience, e.g., "What are the best practices for addressing loss spikes?" The LLM360 K2 project addresses this gap by providing full transparency and access to resources accumulated during the training of LLMs at the largest scale. This report highlights key elements of the K2 project, including our first model, K2 DIAMOND, a 65 billion-parameter LLM that surpasses LLaMA-65B and rivals LLaMA2-70B, while requiring fewer FLOPs and tokens. We detail the implementation steps and present a longitudinal analysis of K2 DIAMOND's capabilities throughout its training process. We also outline ongoing projects such as TXT360, setting the stage for future models in the series. By offering previously unavailable resources, the K2 project also resonates with the 360-degree OPEN SOURCE principles of transparency, reproducibility, and accessibility, which we believe are vital in the era of resource-intensive AI research.

  PublisherOA PDFDOI

• Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer

  ArXiv.org · 2025-02-01 · 1 citations

  preprintOpen access

  Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinically significant cancers. Here we present prostate vision contrastive network (ProViCNet), prostate organ-specific vision foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was trained and validated using 4,401 patients across six institutions, as a prostate cancer detection model on radiology images relying on patch-level contrastive learning guided by biopsy confirmed radiologist annotations. ProViCNet demonstrated consistent performance across multiple internal and external validation cohorts with area under the receiver operating curve values ranging from 0.875 to 0.966, significantly outperforming radiologists in the reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to 0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a virtual screening test, and we showed that we can maintain the high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p<0.001), thereby substantially reducing unnecessary biopsies. These findings highlight that ProViCNet's potential for enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies, thereby optimizing diagnostic pathways.

  PublisherOA PDFDOI

• PD04-11 AI INTERPRETATION OF PROSTATE B-MODE TRANSRECTAL ULTRASOUND CAN LOCALIZE LESIONS SUSPICIOUS FOR PROSTATE CANCER AS WELL AS RADIOLOGISTS INTERPRETING MRI

  The Journal of Urology · 2025-04-08

  article
  PublisherDOI

• Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images

  ArXiv.org · 2025-02-02

  preprintOpen access

  Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by combining MRI's detailed visualization with TRUS's real-time guidance, it is a complex and time-intensive procedure that relies heavily on manual annotations, leading to potential errors. To address these challenges, we propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations. Unlike traditional multimodal fusion approaches that rely on naive data concatenation, our method integrates a registration-segmentation framework to align and leverage spatial information between MRI and TRUS modalities. This alignment enhances segmentation accuracy and reduces reliance on manual effort. Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132) methods, with significant improvements (p $<$ 0.01). This framework demonstrates the potential for reducing the complexity of prostate cancer diagnosis and provides a flexible architecture applicable to other multimodal medical imaging tasks.

  PublisherOA PDFDOI

• ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies

  European Urology Oncology · 2025-01-28 · 5 citations

  articleOpen access
  PublisherOA PDFDOI

• <scp>ProMUS</scp> ‐ <scp>NET</scp> : <scp>Artificial intelligence</scp> detects more prostate cancer than urologists on micro‐ultrasonography

  British Journal of Urology · 2025-08-27 · 4 citations

  article

  OBJECTIVES: To improve sensitivity and inter-reader consistency of prostate cancer localisation on micro-ultrasonography (MUS) by developing a deep learning model for automatic cancer segmentation, and to compare model performance with that of expert urologists. PATIENTS AND METHODS: We performed an institutional review board-approved prospective collection of MUS images from patients undergoing magnetic resonance imaging (MRI)-ultrasonography fusion guided biopsy at a single institution. Patients underwent 14-core systematic biopsy and additional targeted sampling of suspicious MRI lesions. Biopsy pathology and MRI information were cross-referenced to annotate the locations of International Society of Urological Pathology Grade Group (GG) ≥2 clinically significant cancer on MUS images. We trained a no-new U-Net model - the Prostate Micro-Ultrasound Network (ProMUS-NET) - to localise GG ≥2 cancer on these image stacks in a fivefold cross-validation. Performance was compared vs that of six expert urologists in a matched sub-cohort. RESULTS: The artificial intelligence (AI) model achieved an area under the receiver-operating characteristic curve of 0.92 and detected more cancers than urologists (lesion-level sensitivity 73% vs 58%; patient-level sensitivity 77% vs 66%). AI lesion-level sensitivity for peripheral zone lesions was 86.2%. CONCLUSIONS: Our AI model identified prostate cancer lesions on MUS with high sensitivity and specificity. Further work is ongoing to improve margin overlap, to reduce false positives, and to perform external validation. AI-assisted prostate cancer detection on MUS has great potential to improve biopsy diagnosis by urologists.

  PublisherDOI

• MP13-20 PREDICTION OF SEMINAL VESICLE INVASION USING ARTIFICIAL INTELLIGENCE PROSTATE CANCER RISK MAPPING

  The Journal of Urology · 2025-04-08

  article
  PublisherDOI

Frequent coauthors

• Geoffrey A. Sonn

  Stanford University

  121 shared

• Mirabela Rusu

  Stanford University

  77 shared

• Pejman Ghanouni

  Stanford University

  52 shared

• Simon John Christoph Soerensen

  47 shared

• Christian A. Kunder

  Palo Alto University

  37 shared

• James D. Brooks

  Stanford University

  33 shared

• Indrani Bhattacharya

  Technical University of Munich

  31 shared

• Sulaiman Vesal

  Stanford Medicine

  24 shared

Education

• PhD, Biomedical Engineering

  University of California Los Angeles

  2010

• MS, Electrical Engineering

  University of California Los Angeles

  2006

• BS, Electrical Engineering

  University of Arizona

  2005