PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 27, 2024International Journal For Multidisciplinary Research3 citationsOpen Access

The Promising Role of Artificial Intelligence in Navigating Lung Cancer Prognosis

View Full Paper
HQH QureshiY-Yahya Abdul Rehman Shah -S-Sara Muddassir Qureshi -

Key Points

Key points are not available for this paper at this time.

Abstract

Incorporating AI in lung cancer management is a disruptive innovation that has improved diagnosis accuracy, prognosis prediction and treatment modalities. In this literature review, we seek to identify the role of artificial intelligence (AI) and machine learning (ML) in lung cancer detection, diagnosis and treatment between 2010 and 2023. A total of 55 studies were selected systematically from databases such as IEEE Xplore, Scopus and PubMed via a PRISMA-based approach. The analysis reveals that artificial intelligence (AI) techniques, specifically convolutional neural networks (CNNs) and natural language processing (NLP), highly improve the precision of initial detection and imaging of lung cancer. Also, CNN distinguishes between benign and malignant nodules, thus aiding early diagnosis and reducing unnecessary biopsies. On the other hand, NLP is utilized to extract relevant clinical information from electronic health records and unstructured medical texts, thereby enhancing the understanding of patient histories and improving treatment planning. Sensitive and specific scores usually higher than standard techniques characterize these technologies. Results show that traditional statistical approaches couldn’t match AI models whose predictive accuracies are outstanding while providing better care to patients through personalised treatment plans. Furthermore, multi-omics data analysis for personalised treatment planning and clinical decision-making optimisation via Clinical Decision Support Systems (CDSS) powered by AI are some ways artificial intelligence has exhibited its potential in this area. Given this, future studies should aim to fine-tune AI algorithms, improve data integration, and address ethical issues promoting responsible use of AI technologies in clinical practice settings. Despite these advances, data quality, model interpretability, and integration into clinical workflows persist. This review demonstrates the demand for continued research and collaboration from different disciplines so that the complete possibilities of AI in fighting lung cancer may be realised.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Qureshi et al. (2024) studied this question.

synapsesocial.com/papers/68e5ab9cb6db643587545cd6https://doi.org/10.36948/ijfmr.2024.v06i04.26047
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Long Short-Term Memory1997 · 101,538 citations
  2. 2The Role of PET Scan in Diagnosis, Staging, and Management of Non-Small Cell Lung Cancer2004 · 239 citations
  3. 3Machine learning applications in cancer prognosis and prediction2014 · 3,389 citations
  4. 4Amino Acid Deprivation-Induced Autophagy Requires Upregulation of DIRAS3 through Reduction of E2F1 and E2F4 Transcriptional Repression2019 · 26 citations
  5. 5Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer2016 · 1,127 citations