Evidence map›Paper›PMID 40616302›Full record

ReviewFuture science OA2025

Integrating artificial intelligence in healthcare: applications, challenges, and future directions.

Peng Lean Chong, Vikneswaran Vaigeshwari, Basir Khan Mohammed Reyasudin, Binti Ros Azamin Noor Hidayah, Purnshatman Tatchanaamoorti, Jian Ai Yeow, Feng Yuan Kong

Abstract readReview
In one paragraph

Review in Future science OA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 4 pooled it
–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

45 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Article
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  13. Review
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  15. Artificial intelligence in orthopaedics: Enhanced examinations, ambient intelligence and the future of clinical practice.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Review
  16. Review
  17. Article
  18. New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026
    Review
  19. Article
  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

7 authors.

Peng Lean ChongSchool of Engineering and Computing, MILA University, Nilai, Negeri Sembilan, Malaysia.
Vikneswaran VaigeshwariSchool of Engineering and Computing, MILA University, Nilai, Negeri Sembilan, Malaysia.
Basir Khan Mohammed ReyasudinUniversiti Tun Abdul Razak (UNIRAZAK), Kuala Lumpur, Malaysia.
Binti Ros Azamin Noor HidayahSchool of Engineering and Computing, MILA University, Nilai, Negeri Sembilan, Malaysia.
Purnshatman TatchanaamoortiSchool of Engineering and Computing, MILA University, Nilai, Negeri Sembilan, Malaysia.
Jian Ai YeowFaculty of Business, Multimedia University, Melaka, Malaysia.
Feng Yuan KongFaculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnostics across various healthcare domains. This paper explores AI applications in cancer detection, dental medicine, brain tumor database management, and personalized treatment planning. AI technologies such as machine learning and deep learning have enhanced diagnostic accuracy, improved data management, and facilitated personalized treatment strategies. In cancer detection, AI-driven imaging analysis aids in early diagnosis and precise treatment decisions. In dental healthcare, AI applications improve oral disease detection, treatment planning, and workflow efficiency. AI-powered brain tumor databases streamline medical data management, enhancing diagnostic precision and research outcomes. Personalized treatment planning benefits from AI algorithms that analyze genetic, clinical, and lifestyle data to recommend tailored interventions. Despite these advancements, AI integration faces challenges related to data privacy, algorithm bias, and regulatory concerns. Addressing these issues requires improved data governance, ethical frameworks, and interdisciplinary collaboration among healthcare professionals, researchers, and policymakers. Through comprehensive validation, educational initiatives, and standardized protocols, AI adoption in healthcare can enhance patient outcomes and optimize clinical decision-making, advancing the future of precision medicine and personalized care.

Indexed as

Artificial intelligencebrain tumour databasescancer detectiondental healthcarepersonalized treatment planning

Identifiers

PMID40616302
PMCPMC12233828

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.