AI Article Synopsis

  • AI and machine learning are becoming essential in modern healthcare, particularly in spinal care, improving diagnosis, treatment, and patient outcome prediction.
  • They enhance spinal imaging techniques and help create predictive models for personalized treatment plans.
  • Despite their potential, integrating these technologies into clinical practice faces challenges like data quality, security, and ethical issues that need to be resolved for effective use.

Article Abstract

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, and outcome prediction. This review explores their current applications and potential future in the field of spinal care. From enhancing imaging techniques to predicting patient outcomes, AI and ML are revolutionizing the way we approach spinal diseases. AI and ML have significantly improved spinal imaging by augmenting detection and classification capabilities, thereby boosting diagnostic accuracy. Predictive models have also been developed to guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care. Looking towards the future, we envision AI and ML further ingraining themselves in spinal care with the development of algorithms capable of deciphering complex spinal pathologies to aid decision making. Despite the promise these technologies hold, their integration into clinical practice is not without challenges. Data quality, integration hurdles, data security, and ethical considerations are some of the key areas that need to be addressed for their successful and responsible implementation. In conclusion, AI and ML represent potent tools for transforming spinal care. Thoughtful and balanced integration of these technologies, guided by ethical considerations, can lead to significant advancements, ushering in an era of more personalized, effective, and efficient healthcare.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10342311PMC
http://dx.doi.org/10.3390/jcm12134188DOI Listing

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