Area of research
Artificial Intelligence · Oncology
Research interest
Research interests include AI in cancer detection, Cutaneous Melanoma Detection and Management, Radiomics and Machine Learning in Medical Imaging, and Artificial Intelligence in Healthcare and Education.
Prospective Evidence on Artificial Intelligence−Assisted Melanoma Diagnostics
Prompt injection attacks on vision language models in oncology
Discordance, accuracy and reproducibility study of pathologists’ diagnosis of melanoma and melanocytic tumors
ESMO basic requirements for AI-based biomarkers in oncology (EBAI)
Evaluating interactions of patients with large language models for medical information
Deep Learning Reveals Liver MRI Features Associated With <i>PNPLA3</i> I148M in Steatotic Liver Disease
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
Patient insights into empathy, compassion and self-disclosure in medical large language models: results from the IPALLM III study
End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning
Readability of Chatbot Responses in Prostate Cancer and Urological Care: Objective Metrics Versus Patient Perceptions
Enhancing clinicians’ trust in large language models via transparent source attribution: A randomized controlled evaluation in uro-oncology
Large language model use in clinical oncology
Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics
Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images
Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology
Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care
Superhuman performance on urology board questions using an explainable language model enhanced with European Association of Urology guidelines
3-Dimensional Reconstruction From Histopathological Sections: A Systematic Review
Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study
Comparing Patient’s Confidence in Clinical Capabilities in Urology: Large Language Models Versus Urologists
Patients’ and dermatologists’ preferences in artificial intelligence–driven skin cancer diagnostics: A prospective multicentric survey study
Few-shot learning for skin lesion classification: A prototypical networks approach
Predicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides
Automated curation of large‐scale cancer histopathology image datasets using deep learning
Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning
Evaluation of a medical student-delivered smoking prevention program utilizing a face-aging mobile app for secondary schools in Germany: The Education Against Tobacco cluster-randomized controlled trial
Reply to: False conflict and false confirmation errors are crucial components of AI accuracy in medical decision making
Advancing Dermatological Diagnosis: Development of a Hyperspectral Dermatoscope for Enhanced Skin Imaging
Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review
Noninvasive Technologies for the Diagnosis of Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis