PeopleofSuperintelligence

Researcher

Ahmed Hosny

Ahmed Hosny is a medical-imaging researcher whose work applies AI to lung cancer and scientific data. He began in architecture, earned a Bachelor of Architecture at the American University of Sharjah in 2009, and moved into cancer data science while pursuing design technology at Harvard GSD and machine learning at Maastricht. He coauthored CT studies on lung-cancer prognosis and radiotherapy, publications on AI in radiology and radiation oncology, and a Science article on health-care inequalities. In August 2024, he served as CTO of Ambient and principal investigator for an NCI-approved lung-health foundation-model project; he contributed to Data Hub in July 2026 and is a machine learning engineer at Arcadia Science as of October 2026.

Now at
Arcadia Science
Works
62
Citations
16,697
h-index
23
Links
Website, Google Scholar, X, LinkedIn and GitHub

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. By 2017

    Architecture and cancer AI

    He earns a Bachelor of Architecture at the American University of Sharjah in 2009, then works as a project architect at Playze in Shanghai and on Foster + Partners’ Datong Art Museum in Beijing. By 2017, he works in a cancer research institute’s data science lab, builds machine-learning models to predict disease outcomes, and pursues a machine-learning PhD at Maastricht University. At Harvard Graduate School of Design, his graduate work focuses on design technology and material analytics.

  2. 2018

    Lung cancer prognosis and radiology

    In 2018, he coauthors a multi-cohort study with the Department of Radiation Oncology at Dana-Farber Cancer Institute, Brigham and Women’s Hospital and Harvard Medical School; 3D convolutional neural networks use CT images to predict lung-cancer survival. He contributes data curation, formal analysis, software and validation. He also coauthors an opinion article on AI in radiology with a general focus on oncology.

  3. 2019

    Serial CT and health equity

    In 2019, he coauthors a study with colleagues at the Department of Radiation Oncology at Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School; it combines convolutional and recurrent neural networks with serial CT images to predict survival and treatment outcomes in locally advanced lung cancer. He also coauthors the Science article “Artificial intelligence for global health,” on how socially responsible AI could help address health-care inequalities.

  4. 2020

    AI in radiation oncology

    In 2020, he coauthors a Perspective in Nature Reviews Clinical Oncology on artificial intelligence in radiation oncology. It describes the field as highly technical and outlines how AI could affect the radiation-therapy workflow.

  5. 2022

    Lung imaging PhD and validation

    In April 2022, he receives a PhD from Maastricht University for his thesis on deep-learning applications in lung-cancer imaging, supervised by Hugo Aerts and Raymond Mak. In September 2022, he coauthors a clinical-validation study through the Artificial Intelligence in Medicine Program at Mass General Brigham and the Department of Radiation Oncology at Brigham and Women’s Hospital and Dana-Farber Cancer Institute. The study tests deep-learning segmentation of lung tumors and lymph nodes in CT images for radiotherapy planning.

  6. August 2024

    Ambient CTO and lung model

    Before Ambient, he works at Spring Discovery, Dana-Farber Cancer Institute and the Wyss Institute, applying machine learning to biomedical data. In August 2024, he serves as CTO of Ambient, Inc. and principal investigator for the NCI-approved NLST-1309 project to build a foundation model that assesses lung health from CT data.

  7. July 2026

    Data Hub for AI agents

    In July 2026, he contributes supervision and validation to Data Hub, an open-source system for capturing and processing lab-instrument data. Its Model Context Protocol server makes experiment runs accessible to AI agents.

  8. As of October 2026

    Machine learning at Arcadia

    He is a machine learning engineer at Arcadia Science.

Career

  1. NowArcadia ScienceMachine Learning Engineer
  2. From 2024Ambient, Inc.CTO
  3. 2017–2024Dana-Farber Cancer Institute
  4. 2014–2024Harvard University
  5. PlayzeProject Architect

Education

  1. 2022Maastricht UniversityPhD
  2. 2009American University of SharjahBachelor of Architecture
  3. Harvard UniversityGraduate student

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. 11% of AI doctorate holders from 2010 to 2016 founded or led a company or lab by 10 years after graduationIncludes Ahmed Hosny. We estimate how often AI doctorate holders founded or led a company or lab by 10 years after graduation, how waiting times compare between founders and top jobs, and which universities trained the most.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Harvard University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Harvard University. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  4. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Harvard University. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Ahmed Hosny yet. Send us a free-licensed photo.

  1. AUS graduate teaches machines to think like doctors aus.edu. Checked 21 October 2026.
  2. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study journals.plos.org. Checked 21 October 2026.
  3. Artificial intelligence in radiology nature.com. Checked 21 October 2026.
  4. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  5. Artificial intelligence for global health: Socially responsible technologies promise to help address health care inequalities pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  6. Artificial intelligence in radiation oncology nature.com. Checked 21 October 2026.
  7. Deep learning applications in lung cancer imaging cris.maastrichtuniversity.nl. Checked 21 October 2026.
  8. Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  9. NLST-1309: Building a foundation model of lung health in CT data - Approved Projects - The Cancer Data Access System cdas.cancer.gov. Checked 21 October 2026.
  10. Ahmed Hosny ahmedhosny.com. Checked 21 October 2026.
  11. Data Hub: Automated capture and processing of lab instrument data thestacks.org. Checked 21 October 2026.
  12. About | Arcadia Science arcadiascience.com. Checked 21 October 2026.