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AI technologies, particularly in conjunction with OCT and fundus photography, show great potential in diagnosing and treating age-related macular degeneration (AMD). Despite this promise, challenges such as data bias and limited generalizability hinder their effectiveness. A systematic review highlights the progress of AI in this field over the past two decades.
Machine learning and deep learning models have demonstrated comparable accuracy to human experts in assessing AMD risk. The integration of various imaging data types can enhance diagnostic precision, but the reliance on biased datasets poses significant limitations. Future research should focus on improving data quality and expanding the range of cases included in studies.
• AI enhances AMD diagnosis through advanced imaging techniques.
• Data quality issues limit AI's effectiveness in clinical settings.
Machine learning models are used to automate AMD risk assessment, showing accuracy comparable to human observers.
Deep learning models process retinal images directly, minimizing errors and improving AMD identification.
OCT images are analyzed by AI to classify AMD stages and predict treatment efficacy.
Nanchang University contributes to AI research in retinal imaging, enhancing AMD diagnosis.
Beijing Bright Eye Hospital collaborates on AI-driven diagnostic systems for personalized AMD treatment.
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Isomorphic Labs, the AI drug discovery platform that was spun out of Google's DeepMind in 2021, has raised external capital for the first time. The $600
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Sam Altman today revealed that OpenAI will release an open weight artificial intelligence model in the coming months. "We are excited to release a powerful new open-weight language model with reasoning in the coming months," Altman wrote on X.
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