Applied AI and Machine Learning Journal
Vol 1 No 1 (2025): December

Blibiometric analysis of detection lung cancer

Fiqqi Ahludzikri (Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia)
MS. Hasibuan (Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia)
RZ Abdul Aziz (Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia)
Joko Triloka (Institut Informatika dan Bisnis Darmajaya, Lampung, Indonesia)



Article Info

Publish Date
25 Dec 2025

Abstract

Purpose: This study aims to analyze global research trends in lung cancer detection using a bibliometric approach. It focuses on identifying publication growth, dominant research themes, citation patterns, and collaboration networks to better understand the direction and innovation of lung cancer detection research. Methods: A bibliometric analysis was conducted using publication records retrieved from the Scopus database covering the period from 2019 to 2024. Key indicators such as publication output, citation counts, keyword co-occurrence, and author collaboration networks were analyzed. Results: The results indicate a steady increase in publications related to lung cancer detection over the analyzed period. Major research themes include circulating tumor DNA, early detection strategies, next-generation sequencing, and liquid biopsy technologies. The analysis also reveals strong international collaboration networks, highlighting the global nature of lung cancer research and the collective effort to improve detection technologies. Conclusion: The study concludes that research on lung cancer detection is rapidly expanding, driven by technological advancements and growing interest in non-invasive diagnostic approaches. Emerging technologies are expected to play a critical role in enhancing early diagnosis and reducing lung cancer mortality rates. Limitation: This study is limited by its reliance on a single database (Scopus) and a relatively short time frame, which may not capture all relevant publications or long-term research trends. Contribution: This research provides a comprehensive baseline reference for scholars and practitioners, offering valuable insights into current research directions and supporting future advancements in early lung cancer detection methods.

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Journal Info

Abbrev

aiml

Publisher

Subject

Description

Applied AI and Machine Learning Journal (AIML) is a peer-reviewed, open-access scholarly journal dedicated to publishing high-quality original research papers, review articles, and case studies in the fields of artificial intelligence (AI) and machine learning (ML). The journal aims to advance ...