The Epistemological Fragility of Big Medical Data: How Automated Models Fail Clinical Reality

Key Points

• The note critiques the reliability of automated models using big data in clinical contexts, emphasizing the disconnect between data analysis and real-world health scenarios.
• Analyzed two case studies involving unverified clinical signals from large-scale databases.
• Examined the limitations of statistical modeling, particularly Cox regression.
• Discussed implications of these analyses on public health policy.
• Demonstrated that automated models can create misleading epidemiological conclusions.
• Highlighted the risk of e-iatrogenesis (harm caused by electronic medical practices) in public health outcomes.
• Argued for the need for rigorous standards in data verification and model application.