Computer vision algorithms in healthcare: Recent advancements and future challenges

Area of Science:

• Healthcare Technology
• Medical Informatics
• Artificial Intelligence in Medicine

Background:

• Computer vision (CV) is a rapidly advancing field with significant potential to revolutionize healthcare delivery.
• Its applications span various medical domains, offering innovative solutions for diagnosis, treatment, and patient care.

Purpose of the Study:

• To systematically review the current advancements in computer vision applications within the healthcare sector.
• To identify and analyze the key challenges hindering the widespread adoption and effective implementation of CV technologies in healthcare.
• To explore the ethical and legal considerations associated with using CV in medical contexts.

Main Methods:

• A systematic literature review methodology was employed.
• Relevant databases were searched for studies on computer vision in healthcare published within a defined timeframe.
• Selected studies were analyzed to categorize applications, identify challenges, and discuss ethical implications.

Main Results:

• Computer vision has demonstrated substantial progress in medical imaging analysis, surgical assistance, remote patient monitoring, and telehealth platforms.
• Key challenges include ensuring data quality and patient privacy, enhancing model interpretability, and seamless integration with existing healthcare infrastructures.
• Ethical concerns such as obtaining informed patient consent and mitigating algorithmic bias require careful consideration.

Conclusions:

• Computer vision holds immense potential to enhance healthcare delivery and improve patient outcomes.
• Addressing the identified technical, ethical, and regulatory challenges is paramount for the responsible and successful implementation of CV in healthcare.
• Future research should focus on overcoming these hurdles to fully realize the transformative impact of computer vision on medicine.