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Grace Chang is an American technology entrepreneur who co-founded and served as chief executive officer of Kintsugi, a Berkeley, California–based healthcare artificial intelligence company that developed systems designed to analyze acoustic characteristics of human speech as potential indicators of depression and anxiety. Her work has focused on the use of non-semantic vocal biomarkers—such as pitch variability, speech rate, pauses, and other paralinguistic features—to assess mental health–related patterns through measurable acoustic properties of voice.

Chang’s work has been covered in national publications including The New York Times, Forbes, and Fortune.^[1][2][3]

Chang studied computer science and economics at the University of Southern California and later earned a Master of Business Administration from the UCLA Anderson School of Management.

Chang co-founded Kintsugi in 2019. The company developed machine learning systems designed to analyze short samples of free-form speech for acoustic and paralinguistic features rather than linguistic content. The approach emphasized measurable vocal properties associated with physiological and affective states.

Kintsugi’s technology was featured in The New York Times in 2022 in coverage examining artificial intelligence systems designed to detect mental health conditions through voice analysis. Chang was quoted discussing the importance of large and diverse datasets in reducing bias within machine learning models.^[1]

In 2022, Forbes profiled the company’s use of voice biomarkers to detect depression and anxiety, describing its emphasis on acoustic features of speech rather than semantic analysis.^[2]

The company was later included in the Forbes AI 50 list recognizing artificial intelligence companies.^[4]

Research related to Kintsugi’s voice biomarker systems received support through Small Business Innovation Research (SBIR) awards from the U.S. National Science Foundation.^[5]

The research examined whether non-semantic acoustic characteristics of speech could serve as objective indicators of depression severity. Evaluation of an AI-based voice biomarker tool associated with this work was later published in the peer-reviewed journal Annals of Family Medicine in 2025.^[6]

The study evaluated the tool’s performance in identifying signals consistent with moderate to severe depression in clinical settings.

Kintsugi pursued regulatory clearance through the U.S. Food and Drug Administration’s De Novo pathway for medical devices.

In November 2025, Chang participated as a speaker during the Open Public Hearing of the U.S. Food and Drug Administration’s Digital Health Advisory Committee (DHAC) meeting addressing generative artificial intelligence–enabled digital mental health medical devices.^[7]

Chang has discussed challenges related to clinical validation, bias mitigation, and the economic sustainability of regulated artificial intelligence systems in healthcare.^[8]

She has appeared on MIT Technology Review’s podcast In Machines We Trust discussing the use of acoustic modeling and artificial intelligence in mental health assessment.^[9]

• Artificial intelligence in healthcare
• Digital health
• Voice biometrics