Evidence map›Paper›PMID 39878877›Full record

ReviewThe British journal of radiology2025

Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine.

Amanda Isaac, Michail E Klontzas, Danoob Dalili, Asli Irmak Akdogan, Mohamed Fawzi, Giuseppe Gugliemi, Dimitrios Filippiadis

Abstract readReview
In one paragraph

Review in The British journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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

7 authors.

Amanda IsaacSchool of Biomedical Engineering and Imaging Sciences, King's College London, 100 Lambeth Palace Rd, London SE1 7AR, United Kingdom.ORCID 0000-0001-5827-0562
Michail E KlontzasDepartment of Radiology, School of Medicine, University of Crete, Heraklion, Crete, P.C. 71003, Greece.
Danoob DaliliSouthwest London Elective Orthopaedic Centre, Epsom and St Helier Hospitals, Surrey, London SM5 1AA, United Kingdom.
Asli Irmak AkdoganAtaturk Training and Research Hospital, Izmir Katip Çelebi University, Izmir, Turkey.
Mohamed FawziDepartment of Radiology, National Hepatology and Tropical Research Institute, Cairo, Egypt.
Giuseppe GugliemiUniversità Degli Studi di Foggia, Foggia, Italy.
Dimitrios Filippiadis2nd Department of Radiology, University General Hospital "ATTIKON", Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

Indexed as

Artificial IntelligenceBone and BonesBone NeoplasmsImage-Guided BiopsyPrecision MedicineHumansartificial intelligencebone diagnosticsmachine learningosseous biopsypersonalized medicineprecision healthcare

Identifiers

PMID39878877
PMCPMC12089761

What OpenQuestion holds

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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.