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Artificial Intelligence (AI) in Health Sciences


The integration of AI into healthcare brings forth substantial ethical challenges that demand careful consideration. Here are a few major considerations:

  • Bias and Fairness: AI systems can perpetuate or amplify biases from their training data. Learning from historical patient data may inherit past medical practice biases. This can lead to unequal or unfair treatment, especially if certain populations are underrepresented.
  • Transparency and Explainability: Healthcare decisions by AI have profound implications. Patients and clinicians need to understand and trust AI's recommendations. Without transparency, accountability for errors or misjudgments becomes challenging.
  • Data Privacy vs. AI Efficacy: AI requires vast amounts of data for optimal functioning. This need is balanced against a patient's right to data privacy and protection.
  • Global Access and Equity: As AI becomes more integrated, its benefits should be accessible to all. There's an ethical imperative to ensure AI benefits don't only reach the privileged. Preventing a widening of health disparities is crucial.


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