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Artificial intelligence (AI) computer-aided detection (CAD) tools for tuberculosis show promise but require local adaptation. "Tweaking" these algorithms highlights user concerns and political issues crucial for effective global health deployment.

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Area of Science:

  • Global Health
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Computer-aided detection (CAD) algorithms using artificial intelligence (AI) are being developed for tuberculosis (TB) detection.
  • These tools are often proposed as standardized global solutions for TB-endemic regions.

Purpose of the Study:

  • To analyze the tensions between standardizing AI-based CAD algorithms and the need for local adaptation.
  • To explore the political implications of these tensions in TB detection.

Main Methods:

  • Qualitative analysis of practices related to TB detection algorithms in diverse settings.
  • Contrasting perspectives from various stakeholders involved in CAD implementation.

Main Results:

  • AI CAD algorithms offer standardization through automation, aiming to reduce human variability.
  • Local practices, termed "tweaking," reveal how algorithms are situated and adapted.
  • Tweaking highlights user concerns regarding integration into care, infrastructure control, and political ownership.

Conclusions:

  • The "tweaking" of AI CAD algorithms is essential for understanding their situated nature and user concerns.
  • Addressing these local adaptations and political issues is critical for realizing the innovative potential of CAD in global TB management.