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Medical deep learning-A systematic meta-review.

Jan Egger1, Christina Gsaxner2, Antonio Pepe3

  • 1Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Styria, Austria; Department of Oral &Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 5/1, 8036 Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147 Essen, Germany.

Computer Methods and Programs in Biomedicine
|May 19, 2022
PubMed
Summary

Deep learning shows great potential in medicine, but a comprehensive overview is challenging. This meta-review systematically analyzes existing surveys on medical deep learning to provide a high-level perspective.

Keywords:
Artificial neural networksData analysisDeep learningDetectionGenerative adversarial networksImage analysisMachine learningMedical image analysisMedical image processingMedical imagingMeta-reviewMeta-surveyPathologyPatient dataPubMedRegistrationReviewSegmentationSurveySystematic

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Area of Science:

  • Computer Science
  • Medicine
  • Artificial Intelligence

Background:

  • Deep learning (DL) has significantly advanced various scientific fields, including image processing, autonomous driving, and object recognition.
  • The medical domain presents a growing need for automated health information analysis due to increasing patient data and personalized medicine trends.
  • Research in medical deep learning is rapidly expanding, with over 11,000 PubMed results for 'deep learning' in Q2/2020, predominantly from the last three years.

Purpose of the Study:

  • To provide the first high-level, systematic meta-review of existing survey articles on medical deep learning.
  • To consolidate and synthesize the findings from diverse medical deep learning surveys.
  • To offer a comprehensive overview of the medical deep learning landscape, addressing the difficulty in obtaining a complete picture from individual surveys.

Main Methods:

  • Systematic meta-review of published survey articles on medical deep learning.
  • Analysis of existing literature reviews focusing on specific medical applications of deep learning.
  • Synthesis of findings to create a high-level overview of the field.

Main Results:

  • Existing surveys predominantly focus on specific medical scenarios, such as analyzing medical images for pathologies.
  • A comprehensive, high-level overview of the entire field of medical deep learning is currently lacking.
  • The rapid growth of research indicates significant interest and potential for deep learning in healthcare.

Conclusions:

  • Deep learning is a transformative technology with vast potential in the medical field.
  • The current landscape of medical deep learning research is fragmented, with specialized surveys dominating the literature.
  • A systematic meta-review is crucial for understanding the broader impact and future directions of deep learning in medicine.