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Objective physiological data from sensor technology, combined with artificial intelligence (AI) and machine learning (ML), can enhance subjective clinical evaluations. This review explores AI and ML applications in plastic surgery for improved diagnostics and treatments.

Keywords:
Clinical decision makingMachine learningPostoperative painSensor technologySurgical flapWounds and injury

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Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Subjective clinical evaluations are a cornerstone of medical practice.
  • Objective physiological data acquisition is advancing rapidly through sensor technology.
  • Machine learning (ML) offers powerful pattern recognition and predictive capabilities in medicine.

Purpose of the Study:

  • To review the application of sensor technology and ML in plastic surgery.
  • To update knowledge on recent technological advancements in the field.
  • To offer a new perspective on integrating these technologies for improved patient care.

Main Methods:

  • Literature review of sensor technology and machine learning applications.
  • Analysis of current research relevant to plastic surgery.
  • Synthesis of findings to identify trends and future directions.

Main Results:

  • Sensor technology provides objective physiological data as a surrogate for subjective assessments.
  • Machine learning algorithms enhance diagnostic and treatment competencies.
  • Integration of sensor data and ML shows significant potential for plastic surgery.

Conclusions:

  • Combining sensor technology with machine learning offers a promising avenue for advancing plastic surgery.
  • Objective data and AI-driven insights can augment traditional clinical evaluations.
  • Further research and integration are needed to fully realize the potential of these technologies.