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Medical AI is poised to transform healthcare, particularly in diagnosing age-related conditions like Alzheimer's and Parkinson's diseases. Despite advancements, there is a significant gap in how older adults are represented in AI training datasets, which can lead to biases and misdiagnoses. Understanding older adults' perceptions of medical AI is crucial as the population ages and healthcare technologies evolve.
The lack of representation in AI datasets poses challenges in accurately diagnosing conditions prevalent among older adults, such as sleep disorders and cataracts. Engaging older adults in the design and development of medical AI can lead to more effective and user-centered solutions. This approach not only empowers older patients but also ensures that AI technologies are tailored to meet their specific healthcare needs.
• AI technologies must include older adults in their design processes.
• Bias in AI datasets can lead to misdiagnoses in older populations.
Medical AI refers to artificial intelligence applications designed to assist in healthcare diagnosis and treatment.
Bias in AI occurs when algorithms produce skewed results due to unrepresentative training data.
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