Evidence map›Paper›PMID 41625561›Full record

ReviewMedicine international

Metastatic cancer detection and management with artificial intelligence and augmented reality (Review).

Hanisha Reddy Kukunoor, Adithya Andanappa, Kaushalendra Mani Tripathi, Iram Fatima, Ozoemena Z Akah, Ansari Maha Faisal, Fawad Talat, Harsh Bhatia, Arlette Villalobos, Prachi Dawer and 1 more

Abstract readReview
In one paragraph

Review in Medicine international. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Hanisha Reddy KukunoorDepartment of Internal Medicine, Maheshwara Medical College and Hospital, Hyderabad, Telangana 502307, India.
Adithya AndanappaDepartment of Internal Medicine, Mysore Medical College and Research Institute, Mysore, Karnataka 570001, India.
Kaushalendra Mani TripathiDepartment of Internal Medicine, White River Health Medical Center, Batesville, AR 72501, USA.
Iram FatimaDepartment of Internal Medicine, Holy Name Medical Center, Teaneck, NJ 07666, USA.
Ozoemena Z AkahDepartment of Internal Medicine, HCA Mountain View Hospital, Las Vegas, NV 89128, USA.
Ansari Maha FaisalSurat Municipal Institute of Medical Education and Research (SMIMER), Umarwada, Surat, Gujarat 395010, India.
Fawad TalatDepartment of Internal Medicine, Ponce Health Sciences University School of Medicine, Ponce 00716, Puerto Rico.
Harsh BhatiaDepartment of Internal Medicine, University College of Medical Sciences (UCMS), New Delhi 110095, India.
Arlette VillalobosDepartment of Internal Medicine, UHS Wilson Medical Center, Johnson City, NY 13790, USA.
Prachi DawerDepartment of Internal Medicine, Danylo Halytsky L'viv National Medical University, Lviv 79010, Ukraine.
Yusra QamarDepartment of Obstetrics and Gynecology, King George's Medical University (KGMU), Lucknow, Uttar Pradesh 226003, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metastatic cancer remains a significant global health challenge, contributing to the majority of cancer-related mortality due to late detection, therapeutic resistance and the complexity of disseminated disease. Recent advances in artificial intelligence (AI) and augmented reality (AR) are transforming the landscape of metastatic cancer detection and management. AI-driven tools, including radiomics, deep learning models, and predictive analytics, enhance early identification of metastatic lesions, improve diagnostic accuracy, and support personalized treatment strategies by integrating multimodal clinical, imaging and molecular data. At the same time, AR technologies are increasingly applied in image-guided surgery, real-time tumor visualization and patient education, enabling more precise interventions and improved clinical decision-making. The combined use of AI and AR fosters multidisciplinary collaboration, facilitates comprehensive treatment planning, and may ultimately improve patient outcomes. However, despite these advancements, several challenges limit widespread implementation, including algorithmic bias, variability in data quality, concerns regarding patient privacy, and regulatory and ethical constraints. Furthermore, integration into clinical workflows requires robust validation, clinician training, and standardized guidelines. Future efforts are required to focus on developing transparent, generalizable AI models, strengthening data-security frameworks, and enhancing AR usability to ensure equitable, safe, and effective incorporation of these emerging technologies into metastatic cancer care.

Indexed as

algorithmic biasartificial intelligenceaugmented realitymetastatic cancermetastatic diagnoses

Identifiers

PMID41625561
PMCPMC12856537

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.