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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement
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Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.

Rahul Kumar1, Conor Dougherty2, Kyle Sporn3

  • 1Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1600 NW 10th Ave, Miami, FL 33136, USA.

Bioengineering (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) are transforming spine care through advanced diagnostics and personalized treatments. Future directions focus on safe and equitable AI deployment in spine surgery.

Keywords:
artificial intelligencebiomedical informaticsclinical decision supportcomputer visionmachine learningmusculoskeletal imagingneural networksoutcome predictionprecision medicinepredictive modelingspinal diagnosticsspine surgerysurgical robotics

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Area of Science:

  • Spine Surgery
  • Artificial Intelligence
  • Machine Learning

Background:

  • Contemporary spine care is rapidly evolving due to advancements in artificial intelligence (AI) and machine learning (ML).
  • These technologies are impacting diagnostics, surgical planning, risk stratification, and outcome prediction in spine care.
  • The integration of AI and ML promises a paradigm shift towards more personalized and predictive spine treatments.

Purpose of the Study:

  • To synthesize recent advances in AI and ML applications within spine care.
  • To critically evaluate current AI tools and emerging technologies in the field.
  • To identify challenges and outline future directions for AI deployment in spine care.

Main Methods:

  • This narrative review synthesizes information from high-performing AI tools, including convolutional neural networks, robotic guidance platforms, and deep learning systems.
  • It examines the role of ambient clinical intelligence, precision pharmacogenomics, and genome-wide association studies (GWAS).
  • The review also considers multi-omics platforms and federated learning for integrative analytics.

Main Results:

  • AI tools show high performance in areas like vertebral fracture detection and morphometric analysis.
  • Robotic guidance platforms are enhancing surgical precision.
  • Genomic risk stratification and polygenic risk scores are shifting spine surgery towards predictive models.
  • Ambient clinical intelligence and precision pharmacogenomics are enabling personalized care.

Conclusions:

  • AI and ML offer significant potential to improve spine care delivery, from diagnostics to post-operative outcomes.
  • Key challenges include algorithmic opacity, regulatory issues, data heterogeneity, and generalizability.
  • Ensuring safe, equitable, and high-fidelity AI deployment requires addressing these challenges and focusing on future development.