PeopleofSuperintelligence

Researcher

Harrison C. Gottlich

Also known as H. Chase Gottlich, Harrison Chase Gottlich

Harrison C. Gottlich is a physician researcher whose work applies deep learning and machine learning to clinical data collection and medical image segmentation. As a medical student at the Mayo Clinic Alix School of Medicine, he presented a deep-learning project for chronic pelvic pain forms in 2021 and first-authored 2023 studies on renal-tumor segmentation and the AI in the Loop workflow monitor. His 2023 research also examined segmentation model generalization and machine-learning decision support for uterine tumor MRI, alongside urology studies of kidney outcomes in bladder exstrophy and after nephrectomy in 2023 and 2024. He earned an MD from Mayo Clinic College of Medicine in 2024 and, as of October 2026, is a physician in Madison, Wisconsin.

Timeline

  1. 2021

    AI for pelvic-pain data

    In 2021, he presents deep-learning work to automate extraction and quality checking for more than 2,000 retrospective International Pelvic Pain Society chronic pelvic pain assessment forms as a Mayo Clinic Alix School of Medicine student. He holds a B.S. and serves as presenting author.

  2. July 2023

    Generalization in medical image segmentation

    In July 2023, he coauthors an arXiv study evaluating the out-of-domain performance and generalization capabilities of automated medical image segmentation models across new image acquisitions and patient phenotype variations.

  3. August 2023

    Training data for kidney segmentation

    Gottlich is first author of an August 2023 Mayo Clinic study on convolutional neural network segmentation of renal tumors using CT and MR images. Its results show that the training images required to reach a performance plateau vary by imaging modality, target structure and model architecture.

  4. August 2023

    Machine learning for uterine tumors

    An August 2023 arXiv study that he coauthors develops a machine-learning clinical decision support tool for uterine tumor imaging using a curated dataset of 115 axial T2-weighted MRI images from 110 patients across five tumor types.

  5. September 2023

    AI in the Loop

    With Panagiotis Korfiatis, Adriana Gregory and Timothy Kline, he is first author of the September 2023 Frontiers in Radiology paper AI in the Loop on monitoring automated medical image segmentation workflows. The method uses Dice-score disagreement among fivefold cross-validation sub-models as a confidence estimate and compares results with human observer thresholds to flag images for manual review.

  6. 2023

    Kidney disease and bladder exstrophy

    At the 2023 Society for Pediatric Urology Fall Congress, he is a coauthor of an abstract on chronic and end-stage kidney disease in classic bladder exstrophy. Across 197 patients after primary bladder closure at five centers, three patients (1.5%) develop end-stage kidney disease at a median age of 23.4 years.

  7. 2024

    Medical degree and nephrectomy outcomes

    He graduates with an MD from Mayo Clinic College of Medicine in the class of 2024. In October 2024, he coauthors a Mayo Clinic study comparing end-stage kidney disease after partial and radical nephrectomy among patients with severe chronic kidney disease, reporting an ESKD subdistribution hazard ratio of 0.82 for partial versus radical surgery (95% CI 0.50–1.33).

  8. As of October 2026

    Physician in Madison

    As of October 2026, he is a physician in Madison, Wisconsin.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Mayo Clinic. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  3. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. 43% of living people shaping AI work outside their country of birthWe estimate that 43% of living people shaping AI work outside their country of birth (520 of 1,198 people with known birth and employment countries).

Sources and credits

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  1. Abstracts from the International Pelvic Pain Society Annual Scientific Meeting on Pelvic Pain 2021 pmc.ncbi.nlm.nih.gov. Checked 22 October 2026.
  2. Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization arxiv.org. Checked 22 October 2026.
  3. Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study mayoclinic.elsevierpure.com. Checked 22 October 2026.
  4. Developing a Machine Learning-Based Clinical Decision Support Tool for Uterine Tumor Imaging arxiv.org. Checked 22 October 2026.
  5. AI in the Loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows frontiersin.org. Checked 22 October 2026.
  6. End-stage and chronic kidney disease in classic bladder exstrophy fallcongress.spuonline.org. Checked 22 October 2026.
  7. Dr. Harrison Gottlich, MD – Madison, WI | Other MD/DO doximity.com. Checked 22 October 2026.
  8. End-Stage Kidney Disease After Partial and Radical Nephrectomy Among Patients With Severe Chronic Kidney Disease mayoclinic.elsevierpure.com. Checked 22 October 2026.