Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries

Pushpanathan, Krithi; Lim, Zhi Wei; Er Yew, Samantha Min; Chen, David Ziyou; Hui'En Lin, Hazel Anne; Lin Goh, Jocelyn Hui; Wong, Wendy Meihua; Wang, Xiaofei; Jin Tan, Marcus Chun; Chang Koh, Victor Teck; Tham, Yih-Chung

doi:10.1016/j.isci.2023.108163

Cited by 38 publications

(14 citation statements)

References 46 publications

(51 reference statements)

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“…The authors were primarily affiliated with institutions in the United States (n=47 of 122 different countries identified per publication, 38.5%), followed by Germany (n=11/122, 9%), Turkey (n=7/122, 5.7%), the United Kingdom (n=6/122, 4.9%), China/Australia/Italy (n=5/122, 4.1%, respectively), and 24 (n=36/122, 29.5%) other countries. Most studies examined one or more applications based on the GPT-3.5 architecture (n=66 of 124 different LLMs examined per study, 53.2%) 13,26–29,31–34,36–40,42–49,52–54,56–61,63,65–67,71,72,74,75,77,78,81–89,91,92,94,95,97–100,102–104,106–109,111 , followed by GPT-4 (n=33/124, 26.6%) 13,25,27,29,30,34–36,41,43,50,51,54,55,58,61,64,68–70,74,76,79–81,83,87,89,90,93,96,98,99,101,105 , Bard (n=10/124, 8.1%; now known as Gemini) 33,48,49,55,73,74,80,87,94,99 , Bing Chat (n=7/124, 5.7%; now Microsoft Copilot) 49,51,55,73,94,99,110 , and other applications based on Bidirectional Encoder Representations from Transformers (BERT; n=4/124, 3...…”

Section: Resultsmentioning

confidence: 99%

“…A total of 18 (n=18/89, 20.2%) studies reported the presence of conflicts of interest and funding. 13,24,38,40,54,58,59,67,69–71,74,80,84,96,103,105,111 Most studies did not report information about the institutional review board (IRB) approval (n=55/89, 61.8%) or deemed IRB approval unnecessary (n=28/89, 31.5%). Six studies obtained IRB approval (n=6/89, 6.7%).…”

Section: Resultsmentioning

confidence: 99%

“…Most reports evaluated LLMs in English (n=88/89, 98.9%) 13,24–103,105–111 , followed by Arabic (n=2/84, 2.3%) 32,104 , Mandarin (n=2/84, 2.3%) 36,75 , and Korean or Spanish (n=1/89, 1.1%, respectively) 75 . The top-five specialties studied were ophthalmology (n=10/89, 11.2%) 37,40,48,51,65,74,97,98,100,101 , gastro-enterology (n=9/89, 10.1%) 25,32,34,36,39,61,62,72,96 , head and neck surgery/otolaryngology (n=8/89, 9%) 35,42,56,64,66,76,78,79 , and radiology 59,70,88–90,110 or plastic surgery 45,47,49,102,107,108 (n=6/89, 6.7%, respectively). A schematic illustration of the identified concepts of LLM applications in patient care is shown in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

“…The evaluation of limitations in output data yielded 7 second-order codes concerning the non-reproducibility (n=38/89, 42.7%) 28,29,34,38,39,41,43,45,46,49,54–61,64,65,71–73,76,80,82,83,85,90,91,94,96,98,99,101,103–105 , non-comprehensiveness (n=78/89, 87.6%) 13,25,26,28–30,32–44,46,48–62,64,65,67–79,81–98,100,102–107,109–111 , incorrectness (n=78/89, 87.6%) 13,25–44,46,49–52,54–62,64–66,69–79,81–85,87–107,109–111 , (un-)safeness (n=39/89, 43.8%) 28,30,35,37,39,40,42–44,46,50,51,57–60,62,64,65,69,70,73,74,76,78–80,82,84,85,91…”

Section: Resultsmentioning

confidence: 99%

“…Some of the incorrect information could be attributed to what is commonly known as hallucination (n=38/89, 42.7%) 25,28,32,33,35–38,40–44,49–51,57–60,65,73,74,76,77,81,83,85,91,94,96–98,100,103,106,107,109 , i.e., the creation of entirely fictitious or false information that has no basis in the input provided or in reality (e.g., “You may be asked to avoid eating or drinking for a few hours before the scan” for a bone scan). However, numerous instances of misinformation were more appropriately classified under alternative concepts of the original psychiatric analogy, as described in detail by Currie et al 43,112,113 These include illusion (n=12/89, 13.5%) 28,36,38,43,57,59,77,78,85,88,94,105 , which is characterized by the generation of deceptive perceptions or the distortion of information by conflating similar but separate concepts (e.g., suggesting that MRI-type sounds might be experienced during standard nuclear medicine imaging), delirium (n=34/89, 38.2%) 13,26,28,30,37,43,50,58,59,61,65,70,72–75,77,79,81–85,90–92,94,95,98,102,103,107,109,110 , which indicates significant gaps in vital information, resulting in a fragmented or confused understanding of a subject (e.g., omission of crucial information about caffeine cessation for stress myocardial perfusion scans), extrapolation (n=11/89, 12.4%) 43,59,…”

Section: Resultsmentioning

confidence: 99%

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Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges

Busch,

Hoffmann,

Rueger

et al. 2024

Preprint

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The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care using a data-driven convergent synthesis approach. We searched 5 databases for qualitative, quantitative, and mixed methods articles on LLMs in patient care published between 2022 and 2023. From 4,349 initial records, 89 studies across 29 medical specialties were included, primarily examining models based on the GPT-3.5 (53.2%, n=66 of 124 different LLMs examined per study) and GPT-4 (26.6%, n=33/124) architectures in medical question answering, followed by patient information generation, including medical text summarization or translation, and clinical documentation. Our analysis delineates two primary domains of LLM limitations: design and output. Design limitations included 6 second-order and 12 third-order codes, such as lack of medical domain optimization, data transparency, and accessibility issues, while output limitations included 9 second-order and 32 third-order codes, for example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, and bias. In conclusion, this study is the first review to systematically map LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementation and evaluation in healthcare settings.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges

Busch,

Hoffmann,

Rueger

et al. 2024

Preprint

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Testing and Evaluation of Health Care Applications of Large Language Models

Bedi,

Liu,

Orr-Ewing

et al. 2024

JAMA

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ImportanceLarge language models (LLMs) can assist in various health care activities, but current evaluation approaches may not adequately identify the most useful application areas.ObjectiveTo summarize existing evaluations of LLMs in health care in terms of 5 components: (1) evaluation data type, (2) health care task, (3) natural language processing (NLP) and natural language understanding (NLU) tasks, (4) dimension of evaluation, and (5) medical specialty.Data SourcesA systematic search of PubMed and Web of Science was performed for studies published between January 1, 2022, and February 19, 2024.Study SelectionStudies evaluating 1 or more LLMs in health care.Data Extraction and SynthesisThree independent reviewers categorized studies via keyword searches based on the data used, the health care tasks, the NLP and NLU tasks, the dimensions of evaluation, and the medical specialty.ResultsOf 519 studies reviewed, published between January 1, 2022, and February 19, 2024, only 5% used real patient care data for LLM evaluation. The most common health care tasks were assessing medical knowledge such as answering medical licensing examination questions (44.5%) and making diagnoses (19.5%). Administrative tasks such as assigning billing codes (0.2%) and writing prescriptions (0.2%) were less studied. For NLP and NLU tasks, most studies focused on question answering (84.2%), while tasks such as summarization (8.9%) and conversational dialogue (3.3%) were infrequent. Almost all studies (95.4%) used accuracy as the primary dimension of evaluation; fairness, bias, and toxicity (15.8%), deployment considerations (4.6%), and calibration and uncertainty (1.2%) were infrequently measured. Finally, in terms of medical specialty area, most studies were in generic health care applications (25.6%), internal medicine (16.4%), surgery (11.4%), and ophthalmology (6.9%), with nuclear medicine (0.6%), physical medicine (0.4%), and medical genetics (0.2%) being the least represented.Conclusions and RelevanceExisting evaluations of LLMs mostly focus on accuracy of question answering for medical examinations, without consideration of real patient care data. Dimensions such as fairness, bias, and toxicity and deployment considerations received limited attention. Future evaluations should adopt standardized applications and metrics, use clinical data, and broaden focus to include a wider range of tasks and specialties.

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Enhancing patient information texts in orthopaedics: How OpenAI's ‘ChatGPT’ can help

Yüce,

Yerli,

Misir

et al. 2024

J. exp. orthop.

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PurposeThe internet has become a primary source for patients seeking healthcare information, but the quality of online information, particularly in orthopaedics, often falls short. Orthopaedic surgeons now have the added responsibility of evaluating and guiding patients to credible online resources. This study aimed to assess ChatGPT's ability to identify deficiencies in patient information texts related to total hip arthroplasty websites and to evaluate its potential for enhancing the quality of these texts.MethodsIn August 2023, 25 websites related to total hip arthroplasty were assessed using a standardized search on Google. Peer‐reviewed scientific articles, empty pages, dictionary definitions, and unrelated content were excluded. The remaining 10 websites were evaluated using the hip information scoring system (HISS). ChatGPT was then used to assess these texts, identify deficiencies and provide recommendations.ResultsThe mean HISS score of the websites was 9.5, indicating low to moderate quality. However, after implementing ChatGPT's suggested improvements, the score increased to 21.5, signifying excellent quality. ChatGPT's recommendations included using simpler language, adding FAQs, incorporating patient experiences, addressing cost and insurance issues, detailing preoperative and postoperative phases, including references, and emphasizing emotional and psychological support. The study demonstrates that ChatGPT can significantly enhance patient information quality.ConclusionChatGPT's role in elevating patient education regarding total hip arthroplasty is promising. This study sheds light on the potential of ChatGPT as an aid to orthopaedic surgeons in producing high‐quality patient information materials. Although it cannot replace human expertise, it offers a valuable means of enhancing the quality of healthcare information available online.Level of EvidenceLevel IV.

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Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries

Cited by 38 publications

References 46 publications

Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges

Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges

Testing and Evaluation of Health Care Applications of Large Language Models

Enhancing patient information texts in orthopaedics: How OpenAI's ‘ChatGPT’ can help

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