Evidence map›Paper›PMID 41552161›Full record

ReviewCureus2025

Advances in Radiological Imaging Modalities and Their Expanding Role in the Early Diagnosis, Monitoring, and Prognosis of Internal Medicine Disorders: A Comprehensive Review.

Rishitha Rao Ketineni, Bhanupriya Singh, Achsah Raj Chandralekha, Indurani M S, Kanak Soni, Niraj Lodha

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
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.

Rishitha Rao KetineniDepartment of Medicine, Jagadguru Sri Shivarathreeshwara Medical College, Mysore, IND.
Bhanupriya SinghDepartment of Radio-Diagnosis, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, IND.
Achsah Raj ChandralekhaDepartment of Medicine, Christian Medical College and Hospital, Ludhiana, IND.
Indurani M SDepartment of Radio-Diagnosis, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Kattankulathur, IND.
Kanak SoniDepartment of Naturopathy and Yogic Sciences, University of Patanjali, Haridwar, IND.
Niraj LodhaDepartment of Medicine, Lodha Hospital and Research Centre, Pali, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review explores the growing role of advanced radiological imaging in internal medicine, focusing on its applications in prognosis prediction, disease monitoring, and early diagnosis. It highlights how developments in computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, and the integration of artificial intelligence (AI) are reshaping clinical decision-making in fields such as neurology, cardiology, and oncology. Although progress has been substantial, widespread adoption is still limited by high costs, unequal access, and the absence of consistent protocols. The use of AI in combination with radiomics, the quantitative study of medical images, has enhanced diagnostic accuracy and expanded opportunities for outcome prediction and treatment planning. However, challenges remain, including inconsistencies in data quality, regulatory barriers, and the pressing need for validation through large-scale, multicenter studies. Hybrid technologies such as PET/MRI, which combine functional and anatomical imaging, hold particular promise for improving diagnostic precision in neurology and oncology. Together, these innovations illustrate the transformative potential of modern imaging to enable earlier interventions and support more personalized care strategies. The review emphasizes the need for validation, standardized frameworks, and international collaboration to overcome current limitations. Addressing these concerns will broaden the accessibility of these tools, fostering more equitable healthcare and improved outcomes worldwide. Finally, it provides practical guidance for clinicians, researchers, and policymakers who must adapt to the rapidly advancing landscape of radiological imaging.

Indexed as

artificial intelligenceearly diagnosismultimodal imagingpersonalized medicineradiological imaging

Identifiers

PMID41552161
PMCPMC12811736

What Socratic 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 Socratic graph.