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Special issue: informatics & data-driven medicine-2021.

Ivan Izonin1, Nataliya Shakhovska1

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Summary
This summary is machine-generated.

Artificial intelligence (AI) aids medical diagnosis, but struggles with limited patient data. This research presents new AI tools to improve diagnostic accuracy and prognosis using small datasets in medicine.

Keywords:
classificationdata-driven medicinemachine learningoptimizationpredictive modelingsmall datasmall data approach

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Machine Learning for Healthcare

Background:

  • Artificial intelligence (AI) tools are revolutionizing medical diagnosis, treatment, and rehabilitation.
  • Machine learning (ML) and optimization methods, using historical data, offer physicians accurate and rapid automated diagnostic solutions, enhancing medical service quality.
  • A significant challenge in medical AI is the effective diagnosis and prognosis using limited or small datasets.

Purpose of the Study:

  • To address the limitations of existing ML methods in prediction and classification tasks with small datasets.
  • To present novel AI tools and improved existing methods specifically designed for medical diagnostics with limited data.
  • To highlight the urgent need for advanced AI solutions capable of effectively handling sparse medical data.

Main Methods:

  • Development and application of advanced AI and ML algorithms tailored for small numerical and image-based datasets.
  • Utilizing methods with a strong theoretical foundation for medical data analysis.
  • Rigorous experimental validation of the proposed techniques across various medical applications.

Main Results:

  • Demonstrated high efficiency of the described methods in processing small medical datasets.
  • Confirmation of improved prediction and classification model adequacy even with limited training data.
  • Successful application of novel AI tools in diverse fields of medicine.

Conclusions:

  • The developed AI tools offer effective solutions for medical diagnosis and prognosis challenges posed by small datasets.
  • These advancements significantly improve the reliability and accuracy of automated diagnostic systems in resource-limited scenarios.
  • The findings underscore the potential of AI to enhance medical services by overcoming data scarcity issues.