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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial intelligence in diagnostic imaging: collaborative asset or looming replacement?

José Evando da Silva-Filho1,2, André Wescley Oliveira de Aguiar3, Caio Marques Silva4

  • 1Department of Dental Radiology and Imaging, Faculty of Dentistry, University of Fortaleza, 587 Dr. Valmir Pontes Avenue, Edson Queiroz, Fortaleza, Ceará, 60812-020, Brazil. silvafilhoje@gmail.com.

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Summary

Artificial intelligence (AI) in diagnostic imaging offers promise but faces challenges. Responsible implementation requires clear guidelines to ensure AI supports, not replaces, radiologist expertise.

Keywords:
Artificial intelligenceDental imagingDental radiologyDiagnostic imagingRadiology

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Clinical Diagnostics

Background:

  • Artificial intelligence (AI) is rapidly advancing in diagnostic imaging.
  • Key questions arise regarding AI's role: collaboration versus replacement of human expertise.
  • Current evidence on AI applications, clinical, ethical, and legal challenges require review.

Purpose of the Study:

  • To review current evidence on AI applications in diagnostic imaging.
  • To focus on the clinical, ethical, and legal challenges posed by AI.
  • To advocate for responsible AI implementation in healthcare.

Main Methods:

  • Review of current evidence on AI in diagnostic imaging.
  • Discussion of clinical, ethical, and legal challenges.
  • Analysis of financial and practical implications of AI integration.

Main Results:

  • AI models show promise in detecting abnormalities and optimizing workflows.
  • Many AI models are limited by narrow datasets and lack external validation.
  • Ethical issues include algorithm transparency, bias, accountability, and regulatory oversight needs.

Conclusions:

  • Radiologists remain crucial for image interpretation and validating AI outputs.
  • Overreliance on AI risks eroding diagnostic skills; responsible implementation is key.
  • AI should support, not replace, healthcare professionals' expertise and judgment.