JPLab

AI for Early Cancer Detection and Precision Medicine

About the Lab

JPLab develops computational methods to advance early disease detection, personalized screening, and precision medicine. Our research uses longitudinal electronic health records (EHRs), genomic data, and modern AI methods to model patient trajectories and support clinically meaningful decisions.

We work with clinical and computational collaborators across healthcare institutions to develop methods that are robust, interpretable, and relevant to real-world care. We welcome research collaborations and inquiries from prospective trainees.

Principal Investigator

Jiheum Park, PhD

Assistant Professor
Division of Health Informatics and Artificial Intelligence
Department of Population Health Sciences
Weill Cornell Medicine

Email Curriculum Vitae Google Scholar

Research

AI for Early Cancer Detection

We develop EHR-based predictive models and foundation models to identify patients at elevated cancer risk, study transferable signals across cancer types, and support personalized screening and risk stratification across healthcare systems.

Interpretable and Trustworthy Clinical AI

We investigate knowledge-augmented AI, longitudinal patient representations, and clinician-facing interfaces to make predictions more interpretable, evidence-grounded, and useful in clinical decision-making.

Precision Medicine and Treatment Optimization

We combine EHR and genomic information to improve risk assessment and study treatment effectiveness and safety. Our interests include genetic risk prediction, real-world evidence, and causal inference for individualized treatment decisions.

Publications

Data science research in healthcare data

Park J, Pang C, Lee T, Berkowitz J, Wei A, Tatonetti N. Toward Scalable Early Cancer Detection: Evaluating EHR-Based Predictive Models Against Traditional Screening Criteria. npj Precision Oncology. 2026 May 15;10(1):299. PMCID: PMC13424105

Pang C, Park J, Jiang X, Pavinkurve NP, Kalluri KS, Joshi S, Elhadad N, Natarajan K. CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records. arXiv preprint arXiv:2509.03643. 2025 Sep 3.

Park J, Patterson J, Acitores Cortina JM, Gu T, Hur C, Tatonetti N. Enhancing EHR-based pancreatic cancer prediction with LLM-derived embeddings. npj Digital Medicine. 2025 Jul 21;8(1):465. PMCID: PMC12280092

Park J, Artin MG, Lee KE, May BL, Park M, Hur C, Tatonetti NP. Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer. Patterns. 2022 Dec 6:100636. PMCID: PMC9868652

Park J, Artin MG, Lee KE, Pumpalova YS, Ingram MA, May BL, Park M, Hur C, Tatonetti NP. Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer. Journal of Biomedical Informatics. 2022 May, 20:104095. PMCID: PMC10286873

Park J and Bonde PN. Machine Learning in Cardiac Surgery: Predicting Mortality and Readmission. American Society for Artificial Internal Organs. 2022 May. pp. 10-1097. PMID: 35544455

Genomic and immunogenetic research

Park J, Shue S, Zhang D. Genetic Associations in Non-Europeans with Pulmonary Fibrosis: Analysis of the All of Us Cohort. American journal of respiratory and critical care medicine. 2025 Aug 13(ja). PMCID: PMC12410450

Park J, Karnati K, Prest M, Rustgi SD, Hur C, Kong XF, Kastrinos F. Impact of Population Screening for Lynch Syndrome: Insights from the All of Us Data. Nature Communications. 2025 Jan 9;16(1):523. PMCID: PMC11718231

Zheng B, Artin MG, Chung H, Bing C, Sun S, May BL, Hur C, Green PH, Wang TC, Park J, Kong XF. Immunogenetics of gastrointestinal cancers: A systematic review and retrospective survey of inborn errors of immunity in humans. Journal of Gastroenterology and Hepatology. 2022 Apr 5. PMCID: PMC9301767

Computational modeling and simulations

Park J, Lim F, Prest M, Ferris J, Aziz Z, Agyekum A, Wagner S, Gulati R, Hur C. Quantifying the potential benefits of early detection for pancreatic cancer: results of a counterfactual simulation modeling analysis. Scientific Reports. 2023 Nov 16;13(1):20028. PMCID: PMC10654404

Ingram MA, Lauren BN, Pumpalova YS, Park J, Lim F, Bates S, Kastrinos F, Manji GA, Kong CY, Hur Chin. Cost-effectiveness of neoadjuvant FOLFIRINOX vs gemcitabine plus nab-paclitaxel in borderline resectable/locally advanced pancreatic cancer patients. February 2022, Cancer Reports, DOI: 10.1002/cnr2.1565. PMCID: PMC9458514

Park J, Geirsson A, Bonde PN. Mathematical Blueprint of a Mitral Valve. In Seminars in thoracic and cardiovascular surgery. 2019 Jan 8;31(3):399-411. PMID: 30633976.

Shin DA†, Park J†, Lee JC, Do Shin S, Kim HC. Computational Simulation of Passive Leg-Raising Effects on Hemodynamics During Cardiopulmonary Resuscitation. Computer methods and programs in biomedicine. 2017 Mar 1;140:195-200. (†These authors contributed equally to this work.). PMID: 28254076

Software and hardware integration, prototype design, and development

Park J, Oki K, Hesselmann F, Geirsson A, Kaufmann T, Bonde P. Biologically Inspired, Open, Helicoid Impeller Design for Mechanical Circulatory Assist. ASAIO Journal. 2020 Aug;66(8):899-908. doi: 10.1097/MAT.0000000000001090. PMID: 32740350.

Park J, Shin DA, Lee S, Cho YJ, Jheon S, Lee JC, Kim HC. Investigation of key circuit constituents affecting drug sequestration during extracorporeal membrane oxygenation treatment. ASAIO Journal. 2017 May 1;63(3):293-8. PMID: 27922880

Letzen B, Park J, Tuzun Z, Bonde P. Design and Development of a Miniaturized Percutaneously Deployable Wireless Left Ventricular Assist Device: Early Prototypes and Feasibility Testing. ASAIO Journal. 2018 Mar 1;64(2):147-53. PMCID: PMC5823723

Park J, Yoon C, Lee JC, Jung JY, Kim DK, Kwak YH, Kim HC. Manikin-integrated digital measuring system for assessment of infant cardiopulmonary resuscitation techniques. IEEE J Biomed Health Inform. 2014 Sep;18(5):1659-67. doi: 10.1109/JBHI.2013.2288641. Epub 2013 Nov 5. PMID: 24235316.

Image & signal processing

Waters BH†, Park J†, Bouwmeester JC, Valdovinos J, Geirsson A, Sample AP, Smith JR, Bonde P. Electrical power to run ventricular assist devices using the Free-range Resonant Electrical Energy Delivery system. The Journal of Heart and Lung Transplantation. 2018 Dec 1;37(12):1467-74. (†These authors contributed equally to this work.) PMCID: PMC6240491

Bouwmeester JC, Park J, Valdovinos J, Bonde P. Wave intensity analysis of right ventricular function during pulsed operation of rotary left ventricular assist devices. ASAIO Journal. 2019 Jul 1;65(5):465-72. PMCID: PMC6335194

Bouwmeester JC, Park J, Geirsson A, Valdovinos J, Bonde P. Quantification of pulsed operation of rotary left ventricular assist devices with wave intensity analysis. ASAIO Journal. 2019 May 1;65(4):324-30. PMCID: PMC6251778

Park J, Lee GH, Yull Park J, Lee JC, Kim HC. Hypergravity-induced multicellular spheroid generation with different morphological patterns precisely controlled on a centrifugal microfluidic platform. Biofabrication. 2017 Nov 14;9(4):045006. doi: 10.1088/1758-5090/aa9472. PMID: 29045238.

View complete publication list on Google Scholar →

Funding

Active

Agency: NIH NIBIB R01

Title: Scalable Multi-Cancer Early Detection Using EHR Foundation Model with Interactive Clinical Explanations

P.I.: Park, J.

Project period: 09/01/2026 - 07/31/2030

Active

Agency: NIH LLM R03

Title: A Scalable, Open-Source Generative LLM Tool for Automated Classification of Diagnostic Errors

P.I.: Park, J.

Project period: 05/01/2026 - 04/30/2028

Active

Agency: DoD PCARP

Title: Enhancing Early Detection of Pancreatic Cancer Using GPT-Embedded EHR Models: A Novel Approach for Improved Predictive Performance and Interpretability

P.I.: Park, J.

Project period: 08/01/2025 - 07/31/2028

Active

Agency: NIH NCI K25

Title: Early Detection of Pancreatic Cancer with Human-in-the-Loop Deep Learning

P.I.: Park, J.

Project period: 05/01/2023-04/30/2028

Completed

Agency: NIH NCI R21

Title: Domain-Knowledge Informed Deep Learning for Early Detection of Pancreatic Cancer

P.I.: Hur, C.; Tatonetti, N. (Park, J. served as scientific lead and primary proposal developer during postdoctoral training)

Total costs: $432,170

Project period: 07/01/2021-06/30/2023

External Collaborators

Our lab works closely with collaborators across the following institutions:

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Columbia University — Xuhai “Orson” Xu, PhD Matthew McDermott, PhD Fay Kastrinos, MD

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Cedars-Sinai Medical Center — Nicholas Tatonetti, PhD

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U.S. Department of Veterans Affairs — Susan Bates, MD

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Mount Sinai — Keith Sigel, MD, PhD

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University of Birmingham (UK) — Animesh Acharjee

Postdoctoral Opportunities

Postdoctoral Associate

We anticipate recruiting a postdoctoral researcher with a background in computer science, biomedical informatics, data science, statistics, or a related field. Research will focus on AI methods for early cancer detection and precision medicine using longitudinal health data.

Please contact jip7012@med.cornell.edu with your CV and research interests.