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Thomas Lefèvre1

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Summary

Big data in medicine is booming, but genuine studies are rare. Forensic science needs clear definitions and an interdisciplinary approach to leverage big data effectively for evidence-based practice.

Keywords:
Big dataDimensionalityForensic scienceMachine learningPersonalized medicinePredictive medicine

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Area of Science:

  • Biomedical Informatics
  • Forensic Science

Background:

  • Big data in medicine is a growing phenomenon, yet lacks a consensual definition, often relying on the Volume, Variety, and Velocity (3Vs) paradigm.
  • Genuine big data studies in medicine remain scarce and may not fully meet expectations, despite the rise of machine learning techniques.
  • Existing machine learning techniques, often associated with big data, are not novel and have existed for decades.

Discussion:

  • Issues from artificial intelligence and other fields related to big data properties are frequently underestimated in medical applications.
  • A critical perspective on the widespread enthusiasm for big data in medicine is necessary, as some papers highlight significant challenges and risks.
  • Forensic science specifically lacks position papers and a clear outline for integrating big data contributions.

Key Insights:

  • The 3Vs (Volume, Variety, Velocity) paradigm is commonly used but doesn't guarantee genuine big data applications in medicine.
  • Machine learning, while crucial for personalized and predictive medicine, is not exclusive to big data and has a long history.
  • Underestimation of AI-related challenges and the need for critical evaluation temper the big data hype in medicine.

Outlook:

  • There is a pressing need for clear definitions and strategic actions to guide research and practice in big data within medicine and forensic science.
  • An interdisciplinary approach is crucial for forensic science to rationally guide research and practice, integrating evidence-based methods.
  • Forensic science has a significant opportunity to define its unique contributions and position within the broader big data landscape.