• Harvard Ophthalmology AI Lab

    Welcome to the Harvard Ophthalmology AI Lab. We aim to transform eye diseases diagnosis and prognosis with the power of artificial intelligence through our passionate research.


    View our most recent research publications

  • Opportunities

    Find opportunities for collaborations and education.


Equitable AI for disease screening

Our team is dedicated to developing equitable AI models for disease screening with a special focus on ocular diseases. Recently, we have developed a fair identity normalization technique to improve model performance equity.

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AI for medical data cleaning

Our team is dedicated to developing AI-based data cleaning technologies, aiming to provide cleaner data and enhance diagnostic outcomes for eye diseases.

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AI for identifying imaging endophenotypes for GWAS

Our team proposes to uncover the hidden structural patterns of OCT-derived retina layers with two AI models. These patterns are useful for further genetic-wide analysis studies (GWAS).

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Interpretable AI for pathophysiology discovery

Our team aims to design a deep learning model to quantify the retinal layer importance and regional importance of each retinal layer in predicting various ocular and systematic diseases.

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Vision loss detection and progression prediction

Our team aim to better characterize the functional loss from ocular diseases with the goals of developing novel diagnostic methods and improving the progression prediction.

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Relationship between retinal structure and visual function in eye diseases

Our team are investigating the relationship between damages of retinal structure and their precise effects on functional vision in eye diseases.

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Epidemiology and demographic features related to eye diseases

Our team are participating in the analysis work of two large clinical cohort studies. We aim to investigate the interactions of gene, environment, society and personal lifestyle with disease onset and progression.

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Aging and lifestyle affecting the eye

Our team are participating in a population-based study that includes ocular imaging and a large number of physiological and cognitive parameters to systematically investigate the relationship between retinal parameters and age and lifestyle variables.

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What We Do

We work in the intersection among mathematics, computer science, artificial intelligence and clinical ophthalmology. We aim to leverage mathematical, statistical and artificial intelligence modeling to enhance our knowledge and understanding of eye diseases and ultimately improve clinical treatment through our passionate research.

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More Than 200
More Than 6.0
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