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Machine Learning and Artificial Intelligence-Based Clinical Decision Support for Modern Hematology.

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Machine learning (ML) is transforming hematology diagnostics. This review explores ML applications, successes, and limitations across hematology subdomains for healthcare professionals.

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Area of Science:

  • Medical Informatics
  • Clinical Pathology
  • Computational Biology

Background:

  • Hematology is a data-rich medical field.
  • Technological innovation is rapidly advancing hematology.
  • Machine learning (ML) integration into diagnostics is increasing.

Purpose of the Study:

  • To review the current landscape of ML research and clinical applications in hematology.
  • To inform healthcare professionals about ML's impact on workflows.
  • To explore successes and limitations of ML in hematology.

Main Methods:

  • Literature review of ML in hematology.
  • Analysis of research across various hematology subdomains.
  • Examination of clinical applications and deployments.

Main Results:

  • ML shows significant potential in diverse hematology areas.
  • Successes are noted in hematopathology, hemoglobinopathies, and coagulopathy.
  • Limitations and challenges in ML research and deployment are identified.

Conclusions:

  • Understanding ML is crucial for hematology care teams.
  • ML tools require careful integration into clinical workflows.
  • Further research is needed to address ML limitations in hematology.