Pith. sign in

REVIEW 1 cited by

AI Insights: A Case Study on Utilizing ChatGPT Intelligence for Research Paper Analysis

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.03293 v1 pith:IGOVOFBC submitted 2024-03-05 cs.AI

Anjalee De Silva , Janaka L. Wijekoon , Rashini Liyanarachchi , Rrubaa Panchendrarajan , Weranga Rajapaksha This is my paper · ORCID
classification cs.AI
keywords researchwerechatgptgpt-4breastcancerexpertsidentification
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to this topic were collected from three major publication databases Google Scholar, Pubmed, and Scopus. ChatGPT models were used to identify the category, scope, and relevant information from the research papers for automatic identification of relevant papers related to Breast Cancer Treatment (BCT), organization of papers according to scope, and identification of key information for survey paper writing. Evaluations performed using ground truth data annotated using subject experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research paper categories and 50\% of the papers were correctly identified by GPT-4 for their scopes. Further, the results demonstrate that GPT-4 can generate reasons for its decisions with an average of 27\% new words, and 67\% of the reasons given by the model were completely agreeable to the subject experts.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Transformer-based Autoregressive Decoder Architecture for Hierarchical Text Classification

    cs.LG 2025-01 conditional novelty 6.0 of 10

    A decoder that generates child-to-parent symbolic label sequences, with no graph encoder and no label semantics, matches state-of-the-art hierarchical text classification on three benchmarks at roughly half the infere...

Pith tools