Evidence map›Paper›PMID 39058798›Full record

ArticleInternational journal of radiation oncology, biology, physics2024

Current Strengths and Weaknesses of ChatGPT as a Resource for Radiation Oncology Patients and Providers.

Warren Floyd, Troy Kleber, David J Carpenter, Melisa Pasli, Jamiluddin Qazi, Christina Huang, Jim Leng, Bradley G Ackerson, Matthew Pierpoint, Joseph K Salama and 1 more

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Article in International journal of radiation oncology, biology, physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Warren FloydDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Troy KleberDepartment of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
David J CarpenterDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
Melisa PasliBrody School of Medicine, East Carolina University, Greenville, North Carolina.
Jamiluddin QaziDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
Christina HuangDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
Jim LengDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
Bradley G AckersonDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
Matthew PierpointDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina.
Joseph K SalamaDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina.
Matthew J BoyerDepartment of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina; Radiation Oncology Clinical Service, Durham VA Health Care System, Durham, North Carolina. Electronic address: Matthew.Boyer@duke.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeChat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence program that uses natural language processing to generate conversational-style responses to questions or inputs, is increasingly being used by both patients and health care professionals. This study aims to evaluate the accuracy and comprehensiveness of ChatGPT in radiation oncology-related domains, including answering common patient questions, summarizing landmark clinical research studies, and providing literature reviews with specific references supporting current standard-of-care clinical practice in radiation oncology. METHODS AND MATERIALS: We assessed the performance of ChatGPT version 3.5 (ChatGPT3.5) in 3 areas. We evaluated ChatGPT3.5's ability to answer 28 templated patient-centered questions applied across 9 cancer types. We then tested ChatGPT3.5's ability to summarize specific portions of 10 landmark studies in radiation oncology. Next, we used ChatGPT3.5 to identify scientific studies supporting current standard-of-care practice in clinical radiation oncology for 5 different cancer types. Each response was graded independently by 2 reviewers, with discordant grades resolved by a third reviewer.

resultsChatGPT3.5 frequently generated inaccurate or incomplete responses. Only 39.7% of responses to patient-centered questions were considered correct and comprehensive. When summarizing landmark studies in radiation oncology, 35.0% of ChatGPT3.5's responses were accurate and comprehensive, improving to 43.3% when provided the full text of the study. ChatGPT3.5's ability to present a list of studies related to standard-of-care clinical practices was also unsatisfactory, with 50.6% of the provided studies fabricated.

conclusionsChatGPT should not be considered a reliable radiation oncology resource for patients or providers at this time, as it frequently generates inaccurate or incomplete responses. However, natural language programming-based artificial intelligence programs are rapidly evolving, and future versions of ChatGPT or similar programs may demonstrate improved performance in this domain.

Indexed as

Natural Language ProcessingRadiation OncologyArtificial IntelligenceHumansNeoplasmsPatient-Centered Care

Identifiers

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Registered trials

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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.