Feasibility of GPT-3.5 Versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study in Suspected Appendicitis Article Swipe
Sebastian Sanduleanu
,
Koray Ersahin
,
Johannes Bremm
,
Narmin Talibova
,
Tim Damer
,
Merve Erdoğan
,
Jonathan Kottlors
,
Lukas Goertz
,
Christiane J. Bruns
,
David Maintz
,
Nuran Abdullayev
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202409.2358.v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202409.2358.v1
Background: Nonsurgical treatment in uncomplicated appendicitis is in many cases a reasonable option, despite the sparsity of robust, easy access, externally validated and multimodally informed clinical decision support systems (CDSS). Developed by OpenAI, the Generative Pre trained Transformer 3.5 model (GPT 3), may provide enhanced decision support for surgeons in less certain appendicitis cases or those posing a higher risk for (relative) operative contra indications. Our objective was to determine whether GPT 3.5, when provided high throughput clinical, laboratory and radiological text based information will come to similar clinical decisions as a machine learning model and a board certified surgeon (reference standard) in decision making for appendectomy versus conservative treatment. Methods: In this cohort study we randomly collected patients presenting at the Emergency Department (ED) of two German hospitals (GFO, Troisdorf and University Hospital Cologne) with right abdominal pain between October 2022 and October 2023. Statistical analysis was performed using R, version 3.6.2, on RStudio, version 2023.03.0+386. Overall agreement between GPT 3.5 output and the reference standard was assessed by means of inter observer kappa values as well as accuracy, sensitivity, specificity, positive and negative predictive value with the “Caret” and “irr” package. Statistical significance was defined as p < 0.05. Results: There was agreement between surgeon decision and GPT 3.5 in 102 of 113 cases and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT 3.5. The estimated model training accuracy was 83.3 % (95% CI: 74.0, 90.4), while the validation accuracy for the model was 87.0 % (95% CI: 66.4, 97.2). This in comparison to the GPT 3.5 accuracy of 90.3% (95% CI: 83.2, 95.0), which did not perform significantly better in comparison to the machine learning model (P = 0.21). Conclusions: This to our knowledge first “intended use” for surgical treatment GPT 3.5 study comparing surgical decision making versus algorithm found a high degree of agreement between board certified surgeons and GPT 3.5 for surgical decision making in patients presenting to the emergency department with lower abdominal pain.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202409.2358.v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403070208
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403070208Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202409.2358.v1Digital Object Identifier
- Title
-
Feasibility of GPT-3.5 Versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study in Suspected AppendicitisWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-30Full publication date if available
- Authors
-
Sebastian Sanduleanu, Koray Ersahin, Johannes Bremm, Narmin Talibova, Tim Damer, Merve Erdoğan, Jonathan Kottlors, Lukas Goertz, Christiane J. Bruns, David Maintz, Nuran AbdullayevList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202409.2358.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202409.2358.v1Direct OA link when available
- Concepts
-
Appendicitis, Multicenter study, Medicine, Acute appendicitis, Clinical decision making, Medical physics, Artificial intelligence, Radiology, General surgery, Surgery, Computer science, Intensive care medicine, Randomized controlled trialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.cases | 9, 53, 215, 218 |
| abstract_inverted_index.first | 293 |
| abstract_inverted_index.found | 308 |
| abstract_inverted_index.inter | 172 |
| abstract_inverted_index.kappa | 174 |
| abstract_inverted_index.lower | 333 |
| abstract_inverted_index.means | 170 |
| abstract_inverted_index.model | 39, 94, 234, 250, 284 |
| abstract_inverted_index.pain. | 335 |
| abstract_inverted_index.right | 136 |
| abstract_inverted_index.study | 114, 301 |
| abstract_inverted_index.those | 55 |
| abstract_inverted_index.using | 149 |
| abstract_inverted_index.value | 186 |
| abstract_inverted_index.where | 219 |
| abstract_inverted_index.which | 272 |
| abstract_inverted_index.while | 244 |
| abstract_inverted_index.0.21). | 287 |
| abstract_inverted_index.3.6.2, | 152 |
| abstract_inverted_index.90.4), | 243 |
| abstract_inverted_index.95.0), | 271 |
| abstract_inverted_index.97.2). | 257 |
| abstract_inverted_index.German | 127 |
| abstract_inverted_index.better | 277 |
| abstract_inverted_index.cohort | 113 |
| abstract_inverted_index.contra | 63 |
| abstract_inverted_index.degree | 311 |
| abstract_inverted_index.higher | 58 |
| abstract_inverted_index.making | 104, 305, 324 |
| abstract_inverted_index.output | 162 |
| abstract_inverted_index.posing | 56 |
| abstract_inverted_index.use” | 295 |
| abstract_inverted_index.values | 175 |
| abstract_inverted_index.versus | 107, 306 |
| abstract_inverted_index.(CDSS). | 29 |
| abstract_inverted_index.October | 140, 143 |
| abstract_inverted_index.OpenAI, | 32 |
| abstract_inverted_index.Overall | 157 |
| abstract_inverted_index.access, | 19 |
| abstract_inverted_index.between | 139, 159, 205, 314 |
| abstract_inverted_index.certain | 51 |
| abstract_inverted_index.decided | 222 |
| abstract_inverted_index.defined | 196 |
| abstract_inverted_index.despite | 13 |
| abstract_inverted_index.machine | 92, 282 |
| abstract_inverted_index.option, | 12 |
| abstract_inverted_index.perform | 275 |
| abstract_inverted_index.provide | 43 |
| abstract_inverted_index.robust, | 17 |
| abstract_inverted_index.similar | 87 |
| abstract_inverted_index.support | 27, 46 |
| abstract_inverted_index.surgeon | 99, 206, 221 |
| abstract_inverted_index.systems | 28 |
| abstract_inverted_index.trained | 36 |
| abstract_inverted_index.version | 151, 155 |
| abstract_inverted_index.whether | 70 |
| abstract_inverted_index.&lt; | 199 |
| abstract_inverted_index.Cologne) | 134 |
| abstract_inverted_index.Hospital | 133 |
| abstract_inverted_index.Methods: | 110 |
| abstract_inverted_index.RStudio, | 154 |
| abstract_inverted_index.Results: | 201 |
| abstract_inverted_index.accuracy | 236, 247, 265 |
| abstract_inverted_index.analysis | 146 |
| abstract_inverted_index.assessed | 168 |
| abstract_inverted_index.clinical | 25, 88 |
| abstract_inverted_index.decision | 26, 45, 103, 207, 304, 323 |
| abstract_inverted_index.enhanced | 44 |
| abstract_inverted_index.informed | 24 |
| abstract_inverted_index.learning | 93, 283 |
| abstract_inverted_index.negative | 184 |
| abstract_inverted_index.observer | 173 |
| abstract_inverted_index.package. | 192 |
| abstract_inverted_index.patients | 118, 326 |
| abstract_inverted_index.positive | 182 |
| abstract_inverted_index.provided | 74 |
| abstract_inverted_index.randomly | 116 |
| abstract_inverted_index.sparsity | 15 |
| abstract_inverted_index.standard | 166 |
| abstract_inverted_index.surgeons | 48, 317 |
| abstract_inverted_index.surgical | 297, 303, 322 |
| abstract_inverted_index.training | 235 |
| abstract_inverted_index.Developed | 30 |
| abstract_inverted_index.Emergency | 122 |
| abstract_inverted_index.Troisdorf | 130 |
| abstract_inverted_index.abdominal | 137, 334 |
| abstract_inverted_index.accuracy, | 179 |
| abstract_inverted_index.agreement | 158, 204, 313 |
| abstract_inverted_index.algorithm | 307 |
| abstract_inverted_index.certified | 98, 316 |
| abstract_inverted_index.clinical, | 77 |
| abstract_inverted_index.collected | 117 |
| abstract_inverted_index.comparing | 302 |
| abstract_inverted_index.correctly | 227 |
| abstract_inverted_index.decisions | 89 |
| abstract_inverted_index.determine | 69 |
| abstract_inverted_index.emergency | 330 |
| abstract_inverted_index.estimated | 233 |
| abstract_inverted_index.hospitals | 128 |
| abstract_inverted_index.knowledge | 292 |
| abstract_inverted_index.objective | 66 |
| abstract_inverted_index.operative | 62 |
| abstract_inverted_index.performed | 148 |
| abstract_inverted_index.reference | 165 |
| abstract_inverted_index.standard) | 101 |
| abstract_inverted_index.treatment | 2, 225, 298 |
| abstract_inverted_index.validated | 21 |
| abstract_inverted_index.“irr” | 191 |
| abstract_inverted_index.(reference | 100 |
| abstract_inverted_index.(relative) | 61 |
| abstract_inverted_index.Department | 123 |
| abstract_inverted_index.Generative | 34 |
| abstract_inverted_index.University | 132 |
| abstract_inverted_index.classified | 228 |
| abstract_inverted_index.comparison | 260, 279 |
| abstract_inverted_index.department | 331 |
| abstract_inverted_index.externally | 20 |
| abstract_inverted_index.laboratory | 78 |
| abstract_inverted_index.predictive | 185 |
| abstract_inverted_index.presenting | 119, 327 |
| abstract_inverted_index.reasonable | 11 |
| abstract_inverted_index.throughput | 76 |
| abstract_inverted_index.treatment. | 109 |
| abstract_inverted_index.validation | 246 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Nonsurgical | 1 |
| abstract_inverted_index.Statistical | 145, 193 |
| abstract_inverted_index.Transformer | 37 |
| abstract_inverted_index.information | 83 |
| abstract_inverted_index.“Caret” | 189 |
| abstract_inverted_index.“intended | 294 |
| abstract_inverted_index.Conclusions: | 288 |
| abstract_inverted_index.appendectomy | 106 |
| abstract_inverted_index.appendicitis | 5, 52 |
| abstract_inverted_index.conservative | 108, 224 |
| abstract_inverted_index.indications. | 64 |
| abstract_inverted_index.multimodally | 23 |
| abstract_inverted_index.radiological | 80 |
| abstract_inverted_index.sensitivity, | 180 |
| abstract_inverted_index.significance | 194 |
| abstract_inverted_index.specificity, | 181 |
| abstract_inverted_index.significantly | 276 |
| abstract_inverted_index.uncomplicated | 4 |
| abstract_inverted_index.2023.03.0+386. | 156 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.33377277 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |