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Artificial intelligence in modern clinical practice (Review).

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Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances clinical practice through decision support, image analysis, and personalized medicine. However, challenges like data bias, ethical concerns, and regulatory gaps require careful implementation for safe adoption.

Keywords:
artificial intelligencecardiologyclinical decision supportdeep learningdigital pathologyhealthcare ethicsmachine learningmedical imagingprecision medicinerobotic surgery

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Area of Science:

  • Medical Informatics
  • Computational Medicine
  • Digital Health

Background:

  • Artificial intelligence (AI) has evolved from rule-based systems to sophisticated machine learning and deep learning models.
  • The increasing availability of large-scale clinical data fuels AI's integration into healthcare.

Purpose of the Study:

  • To review the current role of artificial intelligence (AI) in modern clinical practice.
  • To identify the opportunities and challenges associated with AI in healthcare.

Main Methods:

  • Literature review synthesizing recent research on AI applications in medicine.
  • Analysis of AI's impact on various medical specialties and patient care.

Main Results:

  • AI tools offer decision support, prediction models, robotic assistance, and image analysis for specialties like radiology, pathology, and dermatology.
  • AI facilitates personalized care in cardiology and precision medicine, improving diagnostic accuracy and optimizing workflows.
  • Potential applications include risk stratification imaging and resource allocation prediction tools.

Conclusions:

  • AI integration promises enhanced precision, optimized tasks, and individualized patient treatment.
  • Critical challenges include algorithmic bias, 'black box' issues impacting trust and accountability, lagging regulations, and implementation costs.
  • Safe and effective adoption necessitates careful planning, validation, and collaboration among diverse stakeholders.