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Doctor/Data Scientist/Artificial Intelligence Communication Model. Case Study.

Smaranda Belciug1, Renato Constantin Ivanescu1, Sebastian-Doru Popa1

  • 1University of Craiova, A.I. Cuza Str, no 13, Craiova, 200585, Romania.

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|December 14, 2022
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Summary
This summary is machine-generated.

The COVID-19 pandemic necessitates new medical practices. This study presents a collaborative model between doctors, computer scientists, and AI for detecting fetal congenital anomalies during second-trimester scans.

Keywords:
computer aided medical diagnosiscongenital anomaliesdeep learningsecond trimester morphologystatistical learningstatistics

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Area of Science:

  • Medical Practice Evolution
  • Computational Medicine
  • Obstetrics and Gynecology

Background:

  • The COVID-19 pandemic significantly impacted healthcare delivery, particularly in obstetrics and gynecology.
  • Effective patient monitoring is crucial for preventing maternal and fetal deaths and complications.
  • Interdisciplinary collaboration is essential for advancing patient care.

Purpose of the Study:

  • To present a novel communication model for integrating medical professionals and AI in prenatal diagnostics.
  • To improve the detection of fetal congenital anomalies during the second-trimester morphology scan.

Main Methods:

  • Development of a competitive/collaborative communication framework.
  • Integration of artificial intelligence systems with clinical workflows.
  • Focus on data scientists and medical doctors working together.

Main Results:

  • Demonstration of a practical model for interdisciplinary communication in obstetrics.
  • Potential for enhanced accuracy and efficiency in identifying fetal anomalies.
  • Improved synergy between human expertise and AI capabilities.

Conclusions:

  • The presented model offers a viable approach to enhance fetal anomaly detection.
  • Collaboration between medical and computational fields is key to future healthcare advancements.
  • Adapting medical practice through technology is vital for improved patient outcomes.