Deep Learning in Medical Image Interpretation: Recent Advances and Impact on Clinical Practice

• DOI: https://doi.org/10.22533/at.ed.1594512405067

• Palavras-chave: Deep Learning; Neural networks; Diagnostic medicine; Artificial intelligence.

• Keywords: Deep Learning; Neural networks; Diagnostic medicine; Artificial intelligence.

• Abstract:

  Objective: The advancement of deep learning and neural network technologies has significantly impacted medical practice, offering new opportunities and challenges. In this introduction, the importance of these technologies in medicine is highlighted, highlighting the need for a comprehensive understanding of their applications, benefits and challenges. The transformative potential of deep learning in medical image analysis is recognized, promoting greater diagnostic accuracy and efficiency in healthcare. Methodology: The methodology adopted in this study was based on a systematic process of selecting relevant scientific articles. Inclusion and exclusion criteria were established to ensure the selection of pertinent studies. The PubMed database was used to perform the bibliographic search, using specific keywords related to the topic of interest. This methodological approach aims to ensure the quality and representativeness of the articles included in the review. Review: The review covered several applications of deep learning in medicine, with a focus on medical image analysis. Highlighted were studies that demonstrated significant improvements in diagnostic accuracy and healthcare efficiency, especially in areas such as radiology, digital pathology and ophthalmology. The technical, ethical and clinical challenges associated with implementing these technologies were also discussed, emphasizing the importance of data quality, model interpretability and ethics in artificial intelligence. 

  Final considerations: Final considerations highlight the revolutionary potential of deep learning in diagnostic medicine, highlighting the need for a careful and collaborative approach for its safe and effective implementation in clinical practice. The importance of overcoming existing challenges is recognized, promoting responsible adoption of these technologies for the benefit of patients and the medical community as a whole.