Exploring ChatGPT's Potential in Facilitating Adaptation of Clinical Guidelines : A Case Study of Diabetic Ketoacidosis Guidelines
Copyright © 2023, Hamed et al..
Background This study aimed to evaluate the efficacy of ChatGPT, an advanced natural language processing model, in adapting and synthesizing clinical guidelines for diabetic ketoacidosis (DKA) by comparing and contrasting different guideline sources. Methodology We employed a comprehensive comparison approach and examined three reputable guideline sources: Diabetes Canada Clinical Practice Guidelines Expert Committee (2018), Emergency Management of Hyperglycaemia in Primary Care, and Joint British Diabetes Societies (JBDS) 02 The Management of Diabetic Ketoacidosis in Adults. Data extraction focused on diagnostic criteria, risk factors, signs and symptoms, investigations, and treatment recommendations. We compared the synthesized guidelines generated by ChatGPT and identified any misreporting or non-reporting errors. Results ChatGPT was capable of generating a comprehensive table comparing the guidelines. However, multiple recurrent errors, including misreporting and non-reporting errors, were identified, rendering the results unreliable. Additionally, inconsistencies were observed in the repeated reporting of data. The study highlights the limitations of using ChatGPT for the adaptation of clinical guidelines without expert human intervention. Conclusions Although ChatGPT demonstrates the potential for the synthesis of clinical guidelines, the presence of multiple recurrent errors and inconsistencies underscores the need for expert human intervention and validation. Future research should focus on improving the accuracy and reliability of ChatGPT, as well as exploring its potential applications in other areas of clinical practice and guideline development.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
|---|---|
| Enthalten in: |
Cureus - 15(2023), 5 vom: 11. Mai, Seite e38784 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Hamed, Ehab [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Revised 13.06.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.7759/cureus.38784 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM358050669 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM358050669 | ||
| 003 | DE-627 | ||
| 005 | 20231226073921.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.7759/cureus.38784 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1193.xml |
| 035 | |a (DE-627)NLM358050669 | ||
| 035 | |a (NLM)37303347 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hamed, Ehab |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Exploring ChatGPT's Potential in Facilitating Adaptation of Clinical Guidelines |b A Case Study of Diabetic Ketoacidosis Guidelines |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 13.06.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2023, Hamed et al. | ||
| 520 | |a Background This study aimed to evaluate the efficacy of ChatGPT, an advanced natural language processing model, in adapting and synthesizing clinical guidelines for diabetic ketoacidosis (DKA) by comparing and contrasting different guideline sources. Methodology We employed a comprehensive comparison approach and examined three reputable guideline sources: Diabetes Canada Clinical Practice Guidelines Expert Committee (2018), Emergency Management of Hyperglycaemia in Primary Care, and Joint British Diabetes Societies (JBDS) 02 The Management of Diabetic Ketoacidosis in Adults. Data extraction focused on diagnostic criteria, risk factors, signs and symptoms, investigations, and treatment recommendations. We compared the synthesized guidelines generated by ChatGPT and identified any misreporting or non-reporting errors. Results ChatGPT was capable of generating a comprehensive table comparing the guidelines. However, multiple recurrent errors, including misreporting and non-reporting errors, were identified, rendering the results unreliable. Additionally, inconsistencies were observed in the repeated reporting of data. The study highlights the limitations of using ChatGPT for the adaptation of clinical guidelines without expert human intervention. Conclusions Although ChatGPT demonstrates the potential for the synthesis of clinical guidelines, the presence of multiple recurrent errors and inconsistencies underscores the need for expert human intervention and validation. Future research should focus on improving the accuracy and reliability of ChatGPT, as well as exploring its potential applications in other areas of clinical practice and guideline development | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ai chatbot | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a chatgpt | |
| 650 | 4 | |a clinical guidelines | |
| 650 | 4 | |a evidence-based medicine | |
| 650 | 4 | |a evidence-based recommendations | |
| 650 | 4 | |a healthcare management | |
| 650 | 4 | |a healthcare technology | |
| 650 | 4 | |a medical informatics | |
| 650 | 4 | |a prompt design | |
| 700 | 1 | |a Eid, Ahmad |e verfasserin |4 aut | |
| 700 | 1 | |a Alberry, Medhat |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Cureus |d 2013 |g 15(2023), 5 vom: 11. Mai, Seite e38784 |w (DE-627)NLM24118083X |x 2168-8184 |7 nnns |
| 773 | 1 | 8 | |g volume:15 |g year:2023 |g number:5 |g day:11 |g month:05 |g pages:e38784 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.7759/cureus.38784 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 15 |j 2023 |e 5 |b 11 |c 05 |h e38784 | ||