Skip to main content

Advertisement

SpringerLink
Log in

Revolutionary Potential of ChatGPT in Constructing Intelligent Clinical Decision Support Systems

  • Letter to the Editor
  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

Recently, Chatbot Generative Pre-trained Transformer (ChatGPT) is recognized as a promising clinical decision support system (CDSS) in the medical field owing to its advanced text analysis capabilities and interactive design. However, ChatGPT primarily focuses on learning text semantics rather than learning complex data structures and conducting real-time data analysis, which typically necessitate the development of intelligent CDSS employing specialized machine learning algorithms. Although ChatGPT cannot directly execute specific algorithms, it aids in algorithm design for intelligent CDSS at the textual level. In this study, besides discussing the types of CDSS and their relationship with ChatGPT, we mainly investigate the benefits and drawbacks of employing ChatGPT as an auxiliary design tool for intelligent CDSS. Our findings indicate that by collaborating with human expertise, ChatGPT has the potential to revolutionize the development of robust and effective intelligent CDSS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Data Availability

Not applicable.

References

  1. Greenes, R. A., D. W. Bates, K. Kawamoto, B. Middleton, J. Osheroff, and Y. Shahar. Clinical decision support models and frameworks: seeking to address research issues underlying implementation successes and failures. J. Biomed. Inform. 78:134–143, 2018.

    Article  PubMed  Google Scholar 

  2. Horng, S., D. A. Sontag, Y. Halpern, Y. Jernite, N. I. Shapiro, and L. A. Nathanson. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE.12:e0174708, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Castaneda, C., K. Nalley, C. Mannion, P. Bhattacharyya, P. Blake, A. Pecora, et al. Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. J. Clin. Bioinformatics. 5:4, 2015.

    Article  Google Scholar 

  4. Pereira, M., P. Concheiro-Moscoso, A. López-Álvarez, G. Baños, A. Pazos, and J. Pereira. Applicability of clinical decision support in management among patients undergoing cardiac surgery in intensive care unit: a systematic review. Appl. Sci. 11:2880, 2021.

    Article  CAS  Google Scholar 

  5. Evans, R. S., S. L. Pestotnik, D. C. Classen, T. P. Clemmer, L. K. Weaver, J. F. Orme, et al. A computer-assisted management program for antibiotics and other antiinfective agents. N. Engl. J. Med. 338:232–238, 1998.

    Article  CAS  PubMed  Google Scholar 

  6. Pombo, N., P. Araújo, and J. Viana. Knowledge discovery in clinical decision support systems for pain management: a systematic review. Artif. Intell. Med. 60:1–11, 2014.

    Article  PubMed  Google Scholar 

  7. Liu, S., A. P. Wright, B. L. Patterson, J. P. Wanderer, R. W. Turer, S. D. Nelson, et al. Using AI-generated suggestions from ChatGPT to optimize clinical decision support. J. Am. Med. Inform. Assoc. 30:ocad072, 2023.

    Article  Google Scholar 

  8. Cheng, K., Q. Guo, Y. He, Y. Lu, S. Gu, and H. Wu. Exploring the potential of GPT-4 in biomedical engineering: the dawn of a new era. Ann. Biomed. Eng. 2023. https://doi.org/10.1007/s10439-023-03221-1.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Liu, G., X. Ma, Y. Zhang, B. Su, and P. Liu. GPT4: the indispensable helper for neurosurgeons in the new era. Ann. Biomed. Eng. 2023. https://doi.org/10.1007/s10439-023-03241-x.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hannun, A. Y., P. Rajpurkar, M. Haghpanahi, G. H. Tison, C. Bourn, M. P. Turakhia, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25:65–69, 2019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ylenia, C., D. L. Chiara, I. Giovanni, R. Lucia, V. Donatella, S. Tiziana, et al. A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients. Math. Biosci. Eng. 18:2654–2674, 2021.

    Article  Google Scholar 

  12. Shi, Z., Z. Liao, and H. Tabata. Enhancing performance of convolutional neural network-based epileptic electroencephalogram diagnosis by asymmetric stochastic resonance. IEEE J. Biomed. Health Inform. 2023. https://doi.org/10.1109/JBHI.2023.3282251.

    Article  PubMed  Google Scholar 

  13. Fernandes, M., S. M. Vieira, F. Leite, C. Palos, S. Finkelstein, and J. M. C. Sousa. Clinical decision support systems for triage in the emergency department using intelligent systems: a review. Artif. Intell. Med.102:101762, 2020.

    Article  PubMed  Google Scholar 

  14. Kashefi, A., and T. Mukerji. ChatGPT for programming numerical methods. J. Mach. Learn. Model. Comput. 2023. https://doi.org/10.1615/JMachLearnModelComput.2023048492.

    Article  Google Scholar 

  15. Sutton, R. T., D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit. Med. 3:17, 2020.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The author acknowledges that certain portions of this manuscript were partially generated by ChatGPT, with the aim of exploring the strengths and weaknesses of ChatGPT in assisting the design of CDSS. The editing work was entirely carried out manually by the author.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Author notes

  1. Zhiqiang Liao and Jian Wang have contributed equally to this work and share the first authorship.

Authors and Affiliations

  1. Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan

    Zhiqiang Liao & Hitoshi Tabata

  2. Department of Orthopaedics, Qilu Hospital of Shandong University, Jinan, 250012, People’s Republic of China

    Jian Wang & Lintao Lu

  3. Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan

    Zhuozheng Shi & Hitoshi Tabata

  4. Department of Orthopaedics, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, People’s Republic of China

    Lintao Lu

Authors
  1. Zhiqiang Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhuozheng Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lintao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hitoshi Tabata
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZL, LL, and HT conceived and designed the manuscript idea. ZL, JW, and ZS wrote the original draft. ZL and JW did the literature search and collected the message, ZS made the figure. LL and HT critically reviewed the manuscript. All authors discussed and approved the final manuscript.

Corresponding authors

Correspondence to Zhiqiang Liao or Lintao Lu.

Ethics declarations

Conflict of interest

The authors declared no conflicts of interest.

Ethical Approval

This study does not include any individual-level data and thus does not require any ethical approval.

Additional information

Associate Editor Stefan M. Duma oversaw the review of this article.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, Z., Wang, J., Shi, Z. et al. Revolutionary Potential of ChatGPT in Constructing Intelligent Clinical Decision Support Systems. Ann Biomed Eng 52, 125–129 (2024). https://doi.org/10.1007/s10439-023-03288-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-023-03288-w

Keywords

Search

Navigation