Advancing the Production of Clinical Medical Devices Through ChatGPT.
ChatGPT
Clinical medical devices
Large language model
Manufacturing engineering
Journal
Annals of biomedical engineering
ISSN: 1573-9686
Titre abrégé: Ann Biomed Eng
Pays: United States
ID NLM: 0361512
Informations de publication
Date de publication:
27 Jun 2023
27 Jun 2023
Historique:
received:
19
06
2023
accepted:
22
06
2023
medline:
28
6
2023
pubmed:
28
6
2023
entrez:
27
6
2023
Statut:
aheadofprint
Résumé
As a recently popular large language model, Chatbot Generative Pre-trained Transformer (ChatGPT) is highly valued in the field of clinical medicine. Due to the limited understanding of the potential impact of ChatGPT on the manufacturing side of clinical medical devices, we aim to fill this gap through this article. We elucidate the classification of medical devices and explore the positive contributions of ChatGPT in various aspects of medical device design, optimization, and improvement. However, limitations such as the potential for misinterpretation of user intent, lack of personal experience, and the need for human supervision should be taken into consideration. Striking a balance between ChatGPT and human expertise can ensure the safety, quality, and compliance of medical devices. This work contributes to the advancement of ChatGPT in the medical device manufacturing industry and highlights the synergistic relationship between artificial intelligence and human involvement in healthcare.
Identifiants
pubmed: 37369944
doi: 10.1007/s10439-023-03300-3
pii: 10.1007/s10439-023-03300-3
doi:
Types de publication
Letter
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
Références
Organization WH. Interagency list of medical devices for essential interventions for reproductive, maternal, newborn and child health. 2016.
Beckmann, U., D. M. Gillies, S. M. Berenholtz, A. W. Wu, and P. Pronovost. Incidents relating to the intra-hospital transfer of critically ill patients. Intensive Care Med. 30:1579–1585, 2004.
doi: 10.1007/s00134-004-2177-9
pubmed: 14991102
Hesse, S., H. Schmidt, C. Werner, and A. Bardeleben. Upper and lower extremity robotic devices for rehabilitation and for studying motor control. Curr. Opin. Neurobiol. 16:705–710, 2003.
doi: 10.1097/00019052-200312000-00010
Liao, Z., S. Jin, A. Kuwahata, M. Sekino, and H. Tabata. Coherent detection stochastic resonance assisted biomagnetometer for measuring magnetocardiography at room temperature. Appl. Phys. Express.14:097001, 2021.
doi: 10.35848/1882-0786/ac1de5
Kimberlin, C. L., and A. G. Winterstein. Validity and reliability of measurement instruments used in research. Am. J. Health Syst. Pharm. 65:2276–2284, 2008.
doi: 10.2146/ajhp070364
pubmed: 19020196
Ali, S. R., T. D. Dobbs, H. A. Hutchings, and I. S. Whitaker. Using ChatGPT to write patient clinic letters. Lancet Digit. Health. 5:e179–e181, 2023.
doi: 10.1016/S2589-7500(23)00048-1
pubmed: 36894409
Savage, N. Drug discovery companies are customizing ChatGPT: here’s how. Nat. Biotechnol. 41:585–586, 2023.
doi: 10.1038/s41587-023-01788-7
pubmed: 37095351
Liao, Z., J. Wang, Z. Shi, L. Lu, and H. Tabata. Revolutionary potential of ChatGPT in constructing intelligent clinical decision support systems. Ann. Biomed. Eng. 2023. https://doi.org/10.1007/s10439-023-03288-w .
doi: 10.1007/s10439-023-03288-w
pubmed: 37332008
Ciurana, J. Designing, prototyping and manufacturing medical devices: an overview. Int. J. Comput. Integr. Manuf. 27:901–918, 2014.
doi: 10.1080/0951192X.2014.934292
Coravos, A., S. Khozin, and K. D. Mandl. Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ Digit. Med. 2:14, 2019.
doi: 10.1038/s41746-019-0090-4
pubmed: 30868107
pmcid: 6411051
Moran, J. M., K. C. Paradis, S. W. Hadley, M. M. Matuszak, C. S. Mayo, K. W. Naheedy, et al. A safe and practical cycle for team-based development and implementation of in-house clinical software. Adv. Radiat. Oncol.7:100768, 2022.
doi: 10.1016/j.adro.2021.100768
pubmed: 35071827
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 .
doi: 10.1109/JBHI.2023.3282251
pubmed: 37347633
Nigar, M. S. S., and Y. S. Mohammed. Use Chat GPT to solve programming bugs. Int. J. Inf. Technol. Comput. Eng. 3:17–22, 2023.
Kashefi, A., and T. Mukerji. ChatGPT for programming numerical methods. J. Mach. Learn. Model. Comput. 2023. https://doi.org/10.1615/JMachLearnModelComput.2023048492 .
doi: 10.1615/JMachLearnModelComput.2023048492
Liao, Z., Z. Wang, H. Yamahara, H. Tabata. Low-power-consumption physical reservoir computing model based on overdamped bistable stochastic resonance system Neurocomputing. 468:137–147, 2022.
doi: 10.1016/j.neucom.2021.09.074
Schuman, C.D., S.R. Kulkarni, M. Parsa, J.P. Mitchell, P. Date, B. Kay. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2:10–19, 2022.
Liao, Z., H. Yamahara, K. Terao, K. Ma, M. Seki, H. Tabata. Short-term memory capacity analysis of Lu