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CC BY-NC-ND 4.0 · Indian J Radiol Imaging
DOI: 10.1055/s-0044-1787974
Original Article

Radiologic Decision-Making for Imaging in Pulmonary Embolism: Accuracy and Reliability of Large Language Models—Bing, Claude, ChatGPT, and Perplexity

Pradosh Kumar Sarangi 
1   Department of Radiodiagnosis, All India Institute of Medical Sciences Deoghar, Deoghar, Jharkhand, India
,
Suvrankar Datta
2   Department of Radiodiagnosis, All India Institute of Medical Sciences New Delhi, New Delhi, India
,
M. Sarthak Swarup
3   Department of Radiodiagnosis, Vardhman Mahavir Medical College and Safdarjung Hospital New Delhi, New Delhi, India
,
Swaha Panda 
4   Department of Otorhinolaryngology and Head and Neck Surgery, All India Institute of Medical Sciences Deoghar, Deoghar, Jharkhand, India
,
Debasish Swapnesh Kumar Nayak 
5   Department of Computer Science and Engineering, SOET, Centurion University of Technology and Management, Bhubaneswar, Odisha, India
,
Archana Malik
6   Department of Pulmonary Medicine, All India Institute of Medical Sciences Deoghar, Deoghar, Jharkhand, India
,
Ananda Datta 
6   Department of Pulmonary Medicine, All India Institute of Medical Sciences Deoghar, Deoghar, Jharkhand, India
,
Himel Mondal 
7   Department of Physiology, All India Institute of Medical Sciences Deoghar, Deoghar, Jharkhand, India
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Abstract

Background Artificial intelligence chatbots have demonstrated potential to enhance clinical decision-making and streamline health care workflows, potentially alleviating administrative burdens. However, the contribution of AI chatbots to radiologic decision-making for clinical scenarios remains insufficiently explored. This study evaluates the accuracy and reliability of four prominent Large Language Models (LLMs)—Microsoft Bing, Claude, ChatGPT 3.5, and Perplexity—in offering clinical decision support for initial imaging for suspected pulmonary embolism (PE).

Methods Open-ended (OE) and select-all-that-apply (SATA) questions were crafted, covering four variants of case scenarios of PE in-line with the American College of Radiology Appropriateness Criteria®. These questions were presented to the LLMs by three radiologists from diverse geographical regions and setups. The responses were evaluated based on established scoring criteria, with a maximum achievable score of 2 points for OE responses and 1 point for each correct answer in SATA questions. To enable comparative analysis, scores were normalized (score divided by the maximum achievable score).

Result In OE questions, Perplexity achieved the highest accuracy (0.83), while Claude had the lowest (0.58), with Bing and ChatGPT each scoring 0.75. For SATA questions, Bing led with an accuracy of 0.96, Perplexity was the lowest at 0.56, and both Claude and ChatGPT scored 0.6. Overall, OE questions saw higher scores (0.73) compared to SATA (0.68). There is poor agreement among radiologists' scores for OE (Intraclass Correlation Coefficient [ICC] = −0.067, p = 0.54), while there is strong agreement for SATA (ICC = 0.875, p < 0.001).

Conclusion The study revealed variations in accuracy across LLMs for both OE and SATA questions. Perplexity showed superior performance in OE questions, while Bing excelled in SATA questions. OE queries yielded better overall results. The current inconsistencies in LLM accuracy highlight the importance of further refinement before these tools can be reliably integrated into clinical practice, with a need for additional LLM fine-tuning and judicious selection by radiologists to achieve consistent and reliable support for decision-making.

Keywords

large language model - American College of Radiology Appropriateness Criteria - pulmonary embolism - Bing - ChatGPT - Claude - Perplexity


Publication History

Article published online:
04 July 2024

© 2024. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Panayides AS, Amini A, Filipovic ND. et al. AI in medical imaging informatics: current challenges and future directions. IEEE J Biomed Health Inform 2020; 24 (07) 1837-1857
    CrossrefPubMedGoogle Scholar
  • 2 Bera K, O'Connor G, Jiang S, Tirumani SH, Ramaiya N. Analysis of ChatGPT publications in radiology: literature so far. Curr Probl Diagn Radiol 2024; 53 (02) 215-225
    CrossrefPubMedGoogle Scholar
  • 3 Tippareddy C, Jiang S, Bera K. et al. Radiology reading room for the future: harnessing the power of large language models like ChatGPT. Curr Probl Diagn Radiol 2023; (e-pub ahead of print) DOI: 10.1067/j.cpradiol.2023.08.018.
    CrossrefPubMedGoogle Scholar
  • 4 Amin KS, Davis MA, Doshi R, Haims AH, Khosla P, Forman HP. Accuracy of ChatGPT, Google Bard, and Microsoft Bing for simplifying radiology reports. Radiology 2023; 309 (02) e232561
    CrossrefPubMedGoogle Scholar
  • 5 Jeblick K, Schachtner B, Dexl J. et al. ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol 2024; 34 (05) 2817-2825
    CrossrefPubMedGoogle Scholar
  • 6 Elkassem AA, Smith AD. Potential use cases for ChatGPT in radiology reporting. Am J Roentgenol 2023; 221 (03) 373-376
    CrossrefPubMedGoogle Scholar
  • 7 Sarangi PK, Lumbani A, Swarup MS. et al. Assessing ChatGPT's proficiency in simplifying radiological reports for healthcare professionals and patients. Cureus 2023; 15 (12) e50881
    PubMedGoogle Scholar
  • 8 Gertz RJ, Bunck AC, Lennartz S. et al. GPT-4 for automated determination of radiological study and protocol based on radiology request forms: a feasibility study. Radiology 2023; 307 (05) e230877
    CrossrefPubMedGoogle Scholar
  • 9 Haver HL, Ambinder EB, Bahl M, Oluyemi ET, Jeudy J, Yi PH. Appropriateness of breast cancer prevention and screening recommendations provided by ChatGPT. Radiology 2023; 307 (04) e230424
    CrossrefPubMedGoogle Scholar
  • 10 Haver HL, Lin CT, Sirajuddin A, Yi PH, Jeudy J. Use of ChatGPT, GPT-4, and Bard to improve readability of ChatGPT's answers to common questions about lung cancer and lung cancer screening. Am J Roentgenol 2023; 221 (05) 701-704
    CrossrefPubMedGoogle Scholar
  • 11 Rahsepar AA, Tavakoli N, Kim GHJ, Hassani C, Abtin F, Bedayat A. How AI responds to common lung cancer questions: ChatGPT vs Google Bard. Radiology 2023; 307 (05) e230922
    CrossrefPubMedGoogle Scholar
  • 12 Bhayana R, Krishna S, Bleakney RR. Performance of ChatGPT on a radiology board-style examination: insights into current strengths and limitations. Radiology 2023; 307 (05) e230582
    CrossrefPubMedGoogle Scholar
  • 13 Sarangi PK, Narayan RK, Mohakud S, Vats A, Sahani D, Mondal H. Assessing the capability of ChatGPT, Google Bard, and Microsoft Bing in solving radiology case vignettes. Indian J Radiol Imaging 2023; 34 (02) 276-282
    PubMedGoogle Scholar
  • 14 Sarangi PK, Irodi A, Panda S, Nayak DSK, Mondal H. Radiological differential diagnoses based on cardiovascular and thoracic imaging patterns: perspectives of four large language models. Indian J Radiol Imaging 2023; 34 (02) 269-275
    PubMedGoogle Scholar
  • 15 Kottlors J, Bratke G, Rauen P. et al. Feasibility of differential diagnosis based on imaging patterns using a large language model. Radiology 2023; 308 (01) e231167
    CrossrefPubMedGoogle Scholar
  • 16 Sun Z, Ong H, Kennedy P. et al. Evaluating GPT4 on impressions generation in radiology reports. Radiology 2023; 307 (05) e231259
    CrossrefPubMedGoogle Scholar
  • 17 Rau A, Rau S, Zoeller D. et al. A context-based chatbot surpasses trained radiologists and generic ChatGPT in following the ACR Appropriateness Guidelines. Radiology 2023; 308 (01) e230970
    CrossrefPubMedGoogle Scholar
  • 18 Patil NS, Huang RS, van der Pol CB, Larocque N. Using artificial intelligence chatbots as a radiologic decision-making tool for liver imaging: Do ChatGPT and Bard communicate information consistent with the ACR Appropriateness Criteria?. J Am Coll Radiol 2023; 20 (10) 1010-1013
    CrossrefPubMedGoogle Scholar
  • 19 Rao A, Kim J, Kamineni M. et al. Evaluating GPT as an adjunct for radiologic decision making: GPT-4 versus GPT-3.5 in a breast imaging pilot. J Am Coll Radiol 2023; 20 (10) 990-997
    CrossrefPubMedGoogle Scholar
  • 20 Doddi S, Hibshman T, Salichs O. et al. Assessing appropriate responses to ACR urologic imaging scenarios using ChatGPT and Bard. Curr Probl Diagn Radiol 2024; 53 (02) 226-229
    CrossrefPubMedGoogle Scholar
  • 21 Markus T, Saban M, Sosna J. et al. Does clinical decision support system promote expert consensus for appropriate imaging referrals? Chest-abdominal-pelvis CT as a case study. Insights Imaging 2023; 14 (01) 45
    CrossrefPubMedGoogle Scholar
  • 22 European Society of Radiology (ESR). Methodology for ESR iGuide content. Insights Imaging 2019; 10 (01) 32
    CrossrefPubMedGoogle Scholar
  • 23 Gabelloni M, Di Nasso M, Morganti R. et al. Application of the ESR iGuide clinical decision support system to the imaging pathway of patients with hepatocellular carcinoma and cholangiocarcinoma: preliminary findings. Radiol Med (Torino) 2020; 125 (06) 531-537
    CrossrefPubMedGoogle Scholar
  • 24 Kjelle E, Andersen ER, Krokeide AM. et al. Characterizing and quantifying low-value diagnostic imaging internationally: a scoping review. BMC Med Imaging 2022; 22 (01) 73
    CrossrefPubMedGoogle Scholar
  • 25 Gamble JL, Ferguson D, Yuen J, Sheikh A. Limitations of GPT-3.5 and GPT-4 in Applying Fleischner Society Guidelines to incidental lung nodules. Can Assoc Radiol J 2023; 75: 412-416
    CrossrefPubMedGoogle Scholar
  • 26 Sarangi PK, Mondal H. Response generated by large language models depends on the structure of the prompt. Indian J Radiol Imaging 2024; (e-pub ahead of print) DOI: 10.1055/s-0044-1782165.
    Article in Thieme ConnectPubMedGoogle Scholar