Evidence map›Paper›PMID 37291063›Full record

ArticleInternational journal of spine surgery2023

Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care.

Samuel R Browd, Christine Park, Daniel A Donoho

Abstract read
In one paragraph

Article in International journal of spine surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Adult spinal deformity correction surgery using age-adjusted alignment thresholds: clinical outcomes and mechanical complication rates. A systematic review of the literature.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024
    Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Samuel R BrowdDepartment of Neurological Surgery, University of Washington, Seattle, WA, USA Samuel.browd@seattlechildrens.org.
Christine ParkDepartment of Neurological Surgery, University of Washington, Seattle, WA, USA.
Daniel A DonohoDivision of Neurological Surgery, Center for Neuroscience and Behavior, Children's National Hospital, Washington, DC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The worlds of spinal surgery and computational science are intersecting at the nexus of the operating room and across the continuum of patient care. As medicine moves toward digitizing all aspects of a patient's care, immense amounts of patient data generated and aggregated across surgeons, procedures, and institutions will enable previously inaccessible computationally driven insights. These early insights from artificial intelligence (AI) and machine learning (ML)-enabled technologies are beginning to transform medicine and surgery. The complex pathologies facing spine surgeons and their patients require integrative, multimodal, data-driven management strategies. As these data and the technological tools to computationally process them become increasingly available to spine surgeons, AI and ML methods will inform patient selection, preoperatively risk-stratify patients based on myriad factors, and inform interoperative surgical decisions. Once these tools enter early clinical practice, their use creates a virtual flywheel whereby the use of these tools generates additional data that further accelerate the evolution of computational "knowledge" systems. At this digital crossroads, interested and motivated surgeons have an opportunity to understand these technologies, guide their application toward optimal care, and advocate for opportunities where these powerful new tools can deliver step changes in efficiency, accuracy, and intelligence. In the present article, we review the nomenclature and basics of AI and ML and highlight the current and future applications of these technologies across the care continuum of spinal surgery.

Indexed as

artificial intelligence (AI)computer visionconvolutional neural networkselectronic medical recordgenerative adversarial networksmachine learning (ML)natural language processing

Identifiers

PMID37291063
PMCPMC10318910

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.