Evidence map›Paper›PMID 40211523›Full record

ArticleNeurospine2025

The Ever-Evolving Regulatory Landscape Concerning Development and Clinical Application of Machine Intelligence: Practical Consequences for Spine Artificial Intelligence Research.

Massimo Bottini, Seung-Jun Ryu, Adrian Elmi Terander, Stefanos Voglis, Nicolai Maldaner, David Bellut, Luca Regli, Carlo Serra, Victor E Staartjes

Abstract read
In one paragraph

Article in Neurospine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Review
  2. 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

9 authors.

Massimo BottiniMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Seung-Jun RyuDepartment of Neurosurgery, Daejeon Eulji University Hospital, Eulji University Medical School, Daejeon, Korea.
Adrian Elmi TeranderDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Stefanos VoglisMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Nicolai MaldanerMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
David BellutMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Luca RegliMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Carlo SerraMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Victor E StaartjesMachine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper analyzes the regulatory frameworks for artificial intelligence/machine learning AI/ML-enabled medical devices in the European Union (EU), the United States (US), and the Republic of Korea, with a focus on applications in spine surgery. The aim is to provide guidance for developers and researchers navigating regulatory pathways. A review of current literature, regulatory documents, and legislative frameworks was conducted. Key differences in regulatory bodies, risk classification, submission requirements, and approval pathways for AI/ML medical devices were examined in the EU, US, and Republic of Korea. The EU AI Act (2024) establishes a risk-based framework, requiring regulatory review based on device risk, with high-risk devices subject to stricter oversight. The US applies a more flexible approach, allowing multiple submission pathways and incorporating a focus on continuous learning. The Republic of Korea emphasizes possibilities of streamlined approval and with growing use of real-world data to support validation. Developers must ensure regulatory alignment early in the development process, focusing on key aspects like dataset quality, transparency, and continuous monitoring. Across all regions, the need for technical documentation, quality management systems, and bias mitigation are essential for approval. Developers are encouraged to adopt adaptable strategies to comply with evolving regulatory standards, ensuring models remain transparent, fair, and reliable. The EU's comprehensive AI Act enforces stricter oversight, while the US and Korea offer more flexible pathways. Developers of spine surgery AI/ML devices must tailor development strategies to align with regional regulations, emphasizing transparent development, quality assurance, and postmarket monitoring to ensure approval success.

Indexed as

Artificial intelligenceMachine learningMedical devicesRegulatory frameworkSpine surgery

Identifiers

PMID40211523
PMCPMC12010853

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.