Evidence map›Paper›PMID 39841330›Full record

ReviewDiscover oncology2025

Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review.

Manjusha Bhange, Darshan Telange

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Review
  19. Review
  20. 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

2 authors.

Manjusha BhangeDepartment of Pharmaceutics, Datta Meghe College of Pharmacy, Datta Meghe Institute of Higher Education and Research (DU), Sawangi Meghe, Wardha, Maharashtra, 442001, India. manjusha.pharmacy@dmiher.edu.in.
Darshan TelangeDepartment of Pharmaceutics, Datta Meghe College of Pharmacy, Datta Meghe Institute of Higher Education and Research (DU), Sawangi Meghe, Wardha, Maharashtra, 442001, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver cancer is one of the most challenging malignancies, often associated with poor prognosis and limited treatment options. Recent advancements in nanotechnology and artificial intelligence (AI) have opened new frontiers in the fight against this disease. Nanotechnology enables precise, targeted drug delivery, enhancing the efficacy of therapeutics while minimizing off-target effects. Simultaneously, AI contributes to improved diagnostic accuracy, predictive modeling, and the development of personalized treatment strategies. This review explores the convergence of nanotechnology and AI in liver cancer treatment, evaluating current progress, identifying existing research gaps, and discussing future directions. We highlight how AI-powered algorithms can optimize nanocarrier design, facilitate real-time monitoring of treatment efficacy, and enhance clinical decision-making. By integrating AI with nanotechnology, clinicians can achieve more accurate patient stratification and treatment personalization, ultimately improving patient outcomes. This convergence holds significant promise for transforming liver cancer therapy into a more precise, individualized, and efficient process. However, data privacy, regulatory hurdles, and the need for large-scale clinical validation remain. Addressing these issues will be essential to fully realizing the potential of these technologies in oncology.

Indexed as

Artificial intelligenceLiver cancerNanotechnologyPersonalized medicine

Identifiers

PMID39841330
PMCPMC11754566

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.