Evidence map›Paper›PMID 39077801›Full record

ReviewCancer prevention research (Philadelphia, Pa.)2024

Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment.

Michael Jacob, Ruhananhad P Reddy, Ricardo I Garcia, Aananya P Reddy, Sachi Khemka, Aryan Kia Roghani, Vasanthkumar Pattoor, Ujala Sehar, P Hemachandra Reddy

Abstract readReview
In one paragraph

Review in Cancer prevention research (Philadelphia, Pa.), 2024. 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

9 authors.

Michael JacobDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0009-0009-5982-6126
Ruhananhad P ReddyDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0009-0004-9713-0262
Ricardo I GarciaDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0009-0001-8470-0121
Aananya P ReddyDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0009-0004-8901-7980
Sachi KhemkaDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0009-0003-6330-3885
Aryan Kia RoghaniDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0009-0008-2102-1229
Vasanthkumar PattoorDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0009-0005-1038-0339
Ujala SeharDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0000-0002-5460-2969
P Hemachandra ReddyDepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas.ORCID 0000-0002-9560-9948

Funding

MicroRNA Mouse Models and Alzheimer’s DiseaseRF1AG079264 · NIA · TEXAS TECH UNIVERSITY HEALTH SCIS CENTER · PI REDDY, P. HEMACHANDRA · 2022 to 2022
$1.9M
National Institutes of Health (NIH) AG079264NIA NIH HHS RF1 AG079264
6 · The paper itself

Abstract

Currently, eight million people in the United States suffer from cancer and it is a major global health concern. Early detection and interventions are urgently needed for all cancers, including colorectal cancer. Colorectal cancer is the third most common type of cancer worldwide. Based on the diagnostic efforts to general awareness and lifestyle choices, it is understandable why colorectal cancer is so prevalent today. There is a notable lack of awareness concerning the impact of this cancer and its connection to lifestyle elements, as well as people sometimes mistaking symptoms for a different gastrointestinal condition. Artificial intelligence (AI) may assist in the early detection of all cancers, including colorectal cancer. The usage of AI has exponentially grown in healthcare through extensive research, and since clinical implementation, it has succeeded in improving patient lifestyles, modernizing diagnostic processes, and innovating current treatment strategies. Numerous challenges arise for patients with colorectal cancer and oncologists alike during treatment. For initial screening phases, conventional methods often result in misdiagnosis. Moreover, after detection, determining the course of which colorectal cancer can sometimes contribute to treatment delays. This article touches on recent advancements in AI and its clinical application while shedding light on why this disease is so common today.

Indexed as

Artificial IntelligenceColorectal NeoplasmsEarly Detection of CancerHumans

Identifiers

PMID39077801
PMCPMC11534518

What OpenQuestion holds

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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.