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The absence of FDA approval for many of these cutting-edge systems highlights the ongoing need for
rigorous clinical validation and regulatory approval processes. While these systems have demonstrated
promising accuracy and efficiency in early studies, they tend to fall prey to similar limitations; challenges
such as high implementation cost, poor situational generalizability, regulatory hurdles, and the need for
human oversight in complex scenarios remain significant barriers to widespread adoption.
The future of spine surgery lies in the continued integration of AI-driven technologies, which can analyze
vast amounts of patient data to inform surgical decisions and predict outcomes. The potential for
personalized surgical approaches, guided by big data analytics and real-time intraoperative monitoring,
holds promise for improving patient care and reducing variability in surgical outcomes. Yet, the ethical and
legal implications of autonomous surgical systems, including issues of liability and accountability, must be
carefully addressed to ensure patient safety. While surgical automation in spine surgery is advancing
rapidly, the full realization of its potential will require overcoming significant challenges. Our current
trajectory suggests a future where autonomous systems will play an increasingly central role in spine
surgery.
DECLARATIONS
Authors’ contributions
Conceived the project: Sadagopan NS, El Tecle NE
Conducted the literature review, drafted the initial manuscript: Sadagopan NS, Prasad D, Jain R
Created the tables: Sadagopan NS
Reviewed and edited the manuscript: Sadagopan NS, Prasad D, Jain R, Ahuja C, Dahdaleh NS, El Tecle NE
All authors read and approved the final manuscript.
Availability of data and materials
Not applicable.
Financial support and sponsorship
None.
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
REFERENCES
1. Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform
2022;23:bbab569. DOI PubMed PMC
2. Nambiar J, Juyal A. Healthcare organizations must create a strong data foundation to fully benefit from generative AI. Available from:
https://www.cio.com/article/1293408/healthcare-organizations-must-create-a-strong-data-foundation-to-fully-benefit-from-generative-
ai.html#. [Last accessed on 5 Nov 2024].
3. Shademan A, Dumont MF, Leonard S, Krieger A, Kim PCW. Feasibility of near-infrared markers for guiding surgical robots. In: