RESUMEN
Background Large language models (LLMs), such as ChatGPT-3.5, Google Bard, and Microsoft Bing, have shown promising capabilities in various natural language processing (NLP) tasks. However, their performance and accuracy in solving domain-specific questions, particularly in the field of hematology, have not been extensively investigated. Objective This study aimed to explore the capability of LLMs, namely, ChatGPT-3.5, Google Bard, and Microsoft Bing (Precise), in solving hematology-related cases and comparing their performance. Methods This was a cross-sectional study conducted in the Department of Physiology and Pathology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India. We curated a set of 50 cases on hematology covering a range of topics and complexities. The dataset included queries related to blood disorders, hematologic malignancies, laboratory test parameters, calculations, and treatment options. Each case and related question was prepared with a set of correct answers to compare with. We utilized ChatGPT-3.5, Google Bard Experiment, and Microsoft Bing (Precise) for question-answering tasks. The answers were checked by two physiologists and one pathologist. They rated the answers on a rating scale from one to five. The average score of the three models was compared by Friedman's test with Dunn's post-hoc test. The performance of the LLMs was compared with a median of 2.5 by a one-sample median test as the curriculum from which the questions were curated has a 50% pass grade. Results The scores among the three LLMs were significantly different (p-value < 0.0001) with the highest score by ChatGPT (3.15±1.19), followed by Bard (2.23±1.17) and Bing (1.98±1.01). The score of ChatGPT was significantly higher than 50% (p-value = 0.0004), Bard's score was close to 50% (p-value = 0.38), and Bing's score was significantly lower than the pass score (p-value = 0.0015). Conclusion The LLMs reveal significant differences in solving case vignettes in hematology. ChatGPT exhibited the highest score, followed by Google Bard and Microsoft Bing. The observed performance trends suggest that ChatGPT holds promising potential in the medical domain. However, none of the models was capable of answering all questions accurately. Further research and optimization of language models can offer valuable contributions to healthcare and medical education applications.
RESUMEN
Malignant gastrointestinal stromal tumor (GIST) is a rare type of sarcoma that is found in the digestive system, most often in the wall of the stomach. Multiple GISTs are extremely rare and usually associated with type 1 neurofibromatosis and familial GIST.We report here a case of a 70-year-old woman who reported pain in the abdomen, loss of appetite, and weight loss for six months. Ultrasound examination showed a small bowel mass along with multiple peritoneal deposits and a mass within the liver. Barium studies were suggestive of a neoplastic pathology of the distal ileum. A differential diagnosis of adenocarcinoma/lymphoma with metastases was entertained. Perioperative findings showed two large growths arising from the jejunum and the distal ileum, along with multiple smaller nodules on the serosal surface and adjoining mesentery of the involved bowel segments. Segmental resection of the involved portions of the intestine was performed. Histopathological features were consistent with those of multicentric malignant GIST-not otherwise specified (GIST-NOS). Follow-up examination three months after surgery showed no evidence of recurrence.
