Evidence map›Paper›PMID 40838224›Full record

ReviewWorld journal of orthopedics2025

Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation.

Aqil M Jawed, Lei Zhang, Zhang Zhang, Qi Liu, Waqas Ahmed, Huan Wang

Abstract readReview
In one paragraph

Review in World journal of orthopedics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
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

6 authors.

Aqil M JawedDepartment of Orthopaedic Surgery, Shengjing Hospital, China Medical University, Shenyang 110004, Liaoning Province, China.
Lei ZhangDepartment of Orthopaedic Surgery, Shengjing Hospital, China Medical University, Shenyang 110004, Liaoning Province, China.
Zhang ZhangDepartment of Orthopaedic Surgery, Shengjing Hospital, China Medical University, Shenyang 110004, Liaoning Province, China.
Qi LiuDepartment of Orthopaedic Surgery, Shengjing Hospital, China Medical University, Shenyang 110004, Liaoning Province, China.
Waqas AhmedSchool of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China.
Huan WangDepartment of Orthopaedic Surgery, Shengjing Hospital, China Medical University, Shenyang 110004, Liaoning Province, China. spinewh@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming spine care by addressing diagnostics, treatment planning, and rehabilitation challenges. This study highlights advancements in precision medicine for spinal pathologies, leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms, enabling faster and more accurate detection of abnormalities. AI-powered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries. Wearable devices and virtual platforms, designed with AI, offer personalized, adaptive therapies that improve treatment adherence and recovery outcomes. AI also enables preventive interventions by assessing spine condition risks early. Despite progress, challenges remain, including limited healthcare datasets, algorithmic biases, ethical concerns, and integration into existing systems. Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI's full potential in spine care. Future developments include multimodal AI systems integrating imaging, clinical, and genetic data for holistic treatment approaches. AI and ML promise significant improvements in diagnostic accuracy, treatment personalization, service accessibility, and cost efficiency, paving the way for more streamlined and effective spine care, ultimately enhancing patient outcomes.

Indexed as

Machine learning diagnosticsPrecision rehabilitationRobotics surgerySpine artificial intelligenceSpine care

Identifiers

PMID40838224
PMCPMC12362650

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.