Publications
83
Citations
1,367
Est. group size
~13
Recurring co-author estimate
Active years
12
Publishing since 2015
Andrew Srisuwananukorn works at the intersection of hematology (blood disorders and blood cancers) and artificial intelligence, focusing on conditions such as myeloproliferative neoplasms, myelofibrosis, leukemia, and sickle cell disease. His research combines clinical trial work on drug therapies for blood cancers with the development of deep learning and vision-language tools for interpreting pathology images, clinical text, and rare-disease data. Prospective students would likely engage with projects spanning clinical hematology-oncology research and computational/AI methods applied to medical data.
Publication output has grown substantially over the last decade, rising from occasional single-digit yearly counts before 2020 to a sustained higher pace of roughly 9-17 papers per year from 2023 onward.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation
arXiv (Cornell University) · 2026
- PBSBench: A Multi-Level Vision-Language Framework and Benchmark for Hematopathology Whole Slide Image Interpretation
arXiv (Cornell University) · 2026
- ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access
Underline Science Inc. · 2026
- When Cases Get Rare: A Retrieval Benchmark for Off-Guideline Clinical Question Answering
arXiv (Cornell University) · 2026
- When Cases Get Rare: A Retrieval Benchmark for Off-Guideline Clinical Question Answering
arXiv (Cornell University) · 2026
- Artificial intelligence in hematology
Blood · 2025
- Preliminary data from the Phase I/II study of nuvisertib, an oral investigational selective PIM1 inhibitor, in combination with momelotinib showed clinical responses in patients with relapsed/refractory myelofibrosis
Blood · 2025
- Safety and efficacy of bromodomain and extra-terminal (BET) inhibitor INCB057643 in patients (pts) with relapsed or refractory myelofibrosis (r/r-MF) and other advanced myeloid neoplasms: A phase (Ph) 1 study.
Journal of Clinical Oncology · 2025
- MPN-780: Safety and Efficacy of Bromodomain and Extra-Terminal (BET) Inhibitor INCB057643 in Patients With Relapsed or Refractory Myelofibrosis (MF) and Other Advanced Myeloid Neoplasms: A Phase 1 Study
Clinical Lymphoma Myeloma & Leukemia · 2025
- POSTER: MPN-780 Safety and Efficacy of Bromodomain and Extra-Terminal (BET) Inhibitor INCB057643 in Patients With Relapsed or Refractory Myelofibrosis (MF) and Other Advanced Myeloid Neoplasms: A Phase 1 Study
Clinical Lymphoma Myeloma & Leukemia · 2025
- Slideflow: deep learning for digital histopathology with real-time whole-slide visualization
BMC Bioinformatics · 2024
- How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned
Orphanet Journal of Rare Diseases · 2024
- Equivalent thrombotic risk with Warfarin, Dabigatran, or Enoxaparin after failure of initial direct oral anticoagulation (DOAC) therapy
Journal of Thrombosis and Thrombolysis · 2024
- Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence
UNC Libraries · 2024
- Deep learning generates synthetic cancer histology for explainability and education
npj Precision Oncology · 2023
- Blood×26
- Clinical Lymphoma Myeloma & Leukemia×9
- arXiv (Cornell University)×7
- eJHaem×3
- bioRxiv (Cold Spring Harbor Laboratory)×3
- Ann‐Kathrin Eisfeld
Medicine · The Ohio State University
- Deedra Nicolet
Medicine · The Ohio State University
- Shivani Handa
Medicine · The Ohio State University
- Payal Desai
Medicine · The Ohio State University
- Roberto F. Machado
Medicine · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
Claim or correct this profile