User:AndreEsteva

Andre Esteva
Andre Esteva
	Andre Esteva 2024 Interview
Born	March 1, 1989; Texas, United States
Nationality	American
Alma mater	University of Texas at Austin Stanford University
Occupation(s)	Entrepreneur, AI scientist
Known for	Medical AI, AI therapy personalization, ArteraAI
Notable work	ArteraAI cancer tests Nature cover on skin cancer AI
Website	andreesteva.com

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Andre Esteva is an American entrepreneur and artificial intelligence (AI) scientist. He is the founder and CEO of ArteraAI, a medical AI company focused on personalizing cancer therapy. ArteraAI's platform, which combines AI interpretation of pathology images with clinical data, has been adopted internationally and integrated into U.S. standard of care guidelines. Esteva has published extensively in the field of medical AI, and his work has been featured in leading journals including Nature and Nature Medicine. He was named to the 2025 TIME100 Health list, to Modern Healthcare’s 40 Under 40 list in 2025 [16], and was selected—along with Artera—by the World Economic Forum [10] for its Global Innovators, a community of the world’s most promising growth-stage companies.

Education

Esteva received dual undergraduate degrees in Electrical Engineering and Pure Mathematics from the University of Texas at Austin, graduating with highest honors. His undergraduate thesis focused on invisibility cloaking using metamaterials, and he authored the thesis "Metamaterial Structural Design." [22] He was awarded the Outstanding Scholar-Leader Award (Engineering Valedictorian) by UT-Austin. [1] He completed his Ph.D. in Artificial Intelligence at Stanford University, where he conducted pioneering research in medical AI. His Ph.D. was supported by both the Department of Energy Office of Science Graduate Fellowship Program and the Stanford Graduate Fellowship. He was a member of the Stanford AI Lab and was co-advised by Sebastian Thrun and Stephen P. Boyd.

During his doctoral studies, Esteva published a landmark study on the cover of Nature that was the first-ever scientific paper to demonstrate that artificial intelligence could match the diagnostic performance of clinicians. [2] His team developed a mobile app powered by deep learning algorithms that could diagnose skin cancer as accurately as board-certified dermatologists. The model was evaluated on clinical cases, including testing at Stanford Hospital, and marked a breakthrough in AI for medical diagnostics. The publication remains, to this day, the 4th most read Nature paper of a similar age. [26] The work received widespread media attention and was featured in outlets such as Fortune [23] and The Wall Street Journal [24]. It was later recognized by Nature as a "Milestone in Cancer" over the past 20 years. [25]

Career

Early Career

After completing his undergraduate degrees, Esteva spent a year at Sandia National Laboratories working on Z-pinch nuclear fusion experiments on the Z Machine. During his undergraduate studies, he also held a research role at GE Healthcare. While completing his Ph.D. at Stanford, he conducted research at Google Research, where he co-authored a paper in Cell that was the first to demonstrate how AI could use microscopy images of tissue structure to accurately render chemical composition. This approach enabled near-perfect inference of tissue chemistry without the need for traditional chemical staining, marking a breakthrough in digital histopathology. [21]

Cresta

While at Stanford, Esteva co-founded and served as Chief Operating Officer (COO) of Cresta, an enterprise AI company developing generative and agentic AI solutions for contact centers. Cresta was spun out of his Stanford lab and uses AI to analyze conversations, assist agents with real-time suggestions, and automate workflows to improve performance and efficiency. The company later achieved unicorn status, reaching a valuation of $1.6 billion following its Series C funding round. [19][20]

Salesforce Research

Esteva served as Head of Medical AI at Salesforce Research, where he led efforts to integrate artificial intelligence into Salesforce's healthcare and life sciences products. During his tenure, he conducted basic research in medical AI and published numerous high-impact scientific papers in journals such as Nature Medicine, Nature Digital Medicine, and The Lancet.[12][13][19][20][21][22][23][24][25][26] His research focused on applying deep learning to predict treatment outcomes, stratify patient risk, and guide clinical trial design using real-world clinical data and electronic health records.

During this time, Salesforce CEO Marc Benioff introduced Esteva to Felix Feng, a leading academic in prostate cancer research. Their collaboration resulted in early foundational research that would later become the basis of ArteraAI's multimodal AI platform. The research was initially developed within Salesforce Research before being spun out to form ArteraAI.

ArteraAI

Esteva co-founded ArteraAI in 2021 to develop AI-driven therapy personalization for cancer care. In 2023, the company launched with $90 million in funding across its seed and Series A rounds. [3] A Series A1 round of $20 million followed in early 2024. [4]

ArteraAI developed the first-ever predictive biomarker for localized prostate cancer to be included in the NCCN Guidelines, where it is now the only predictive test for therapy personalization in prostate cancer. [5] The test has been adopted by over 20% of prostate cancer clinicians in the United States as of 2025. The test also received Medicare reimbursement. [6] A prospective Phase III clinical trial, PROSTATE-IQ—conducted in collaboration with MD Anderson and Johnson & Johnson—uses ArteraAI’s test to stratify patients for treatment. [27] Artera has since expanded to develop predictive and prognostic AI tests across multiple cancer types, including prostate, breast, head and neck, and others—covering both localized and metastatic disease. The company has published over 40 pieces of clinical evidence as of 2025. [7]

The company has expanded internationally, launching in the United Kingdom through a partnership with Diagnexia. It also expanded to Australia in partnership with GenesisCare, one of the largest global providers of radiation oncology, to launch the first Australian-based clinical trial. [28]

Publications

Esteva has published over 75 scientific works that have been cited more than 25,000 times. [11] His body of work focuses on medical AI, with a particular emphasis on diagnostics and therapy personalization.

Select Publications

Spratt DE, Tang S, Sun Y, et al. "Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer." New England Journal of Medicine Evidence, July 2023. [14]

Vasey B, Nagendran M, Campbell B, et al. "Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI." Nature Medicine, May 2022. [12]

Esteva A, Feng J, van der Wal D, et al. "Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials." Nature Digital Medicine, June 2022. [11]

Sounderajah V, Ashrafian H, Rose S, et al. "A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI." Nature Medicine, October 2021. [12]

van der Wal D, Jhun I, Laklouk I, et al. "Biological data annotation via a human-augmenting AI-based labeling system." Nature Digital Medicine, October 2021. [12]

Esteva A, Kale A, Paulus R, et al. "CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization." Nature Digital Medicine, April 2021. [12]

Esteva A, Chou K, Yeung S, et al. "Deep learning-enabled medical computer vision." Nature Digital Medicine, January 2021. [12]

Naik N, Madani A, Esteva A, et al. "Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains." Nature Communications, November 2020. [14]

Liu X, Rivera SC, Moher D, et al. "Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension." Nature Medicine, September 2020. [13]

Esteva A, Topol E. "Can skin cancer diagnosis be transformed by AI?" The Lancet, November 2019. [14]

Esteva A, Robicquet A, Ramsundar B, et al. "A guide to deep learning in healthcare." Nature Medicine, January 2019. [12]

Christiansen E, Yang S, Ando D, et al. "In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images." Cell, April 2018. [21]

Esteva A, Kuprel B, Novoa RA, et al. "Dermatologist-level classification of skin cancer with deep neural networks." Nature, January 2017. [2]