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- From "MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering"
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- (Pal et al.), MedMCQA is a "multiple-choice question answering (MCQA) dataset designed to address
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- real-world medical entrance exam questions." The dataset "...has more than 194k high-quality AIIMS & NEET PG
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- entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average
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- token length of 12.77 and high topical diversity."
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- The following is an example from the dataset:
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- Question: In a patient of heart disease antibiotic prophylaxis for dental extraction is:
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- A. Amoxicillin.
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- B. Imipenem.
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- C. Gentamicin.
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- D. Erythromycin.
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- Answer: A
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- Paper: https://arxiv.org/abs/2203.14371
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- Code: https://github.com/MedMCQA/MedMCQA
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  ```
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- @InProceedings{pmlr-v174-pal22a,
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- title = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
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- author = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
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- booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
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- pages = {248--260},
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- year = {2022},
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- editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},
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- volume = {174},
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- series = {Proceedings of Machine Learning Research},
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- month = {07--08 Apr},
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- publisher = {PMLR},
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- pdf = {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},
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- url = {https://proceedings.mlr.press/v174/pal22a.html},
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- abstract = {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset
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- designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS & NEET PG
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- entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token
36
- length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other
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- options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across
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- a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above
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- information, is provided in this study.}
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- }
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  ```
 
1
+ From "MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering"
2
+ (Pal et al.), MedMCQA is a "multiple-choice question answering (MCQA) dataset designed to address
3
+ real-world medical entrance exam questions." The dataset "...has more than 194k high-quality AIIMS & NEET PG
4
+ entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average
5
+ token length of 12.77 and high topical diversity."
6
 
7
+ The following is an example from the dataset:
8
 
9
+ Question: In a patient of heart disease antibiotic prophylaxis for dental extraction is:
10
+ A. Amoxicillin.
11
+ B. Imipenem.
12
+ C. Gentamicin.
13
+ D. Erythromycin.
14
+ Answer: A
15
 
16
+ Paper: https://arxiv.org/abs/2203.14371
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+ Code: https://github.com/MedMCQA/MedMCQA
18
 
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  ```
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+ @InProceedings{pmlr-v174-pal22a,
21
+ title = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
22
+ author = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
23
+ booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
24
+ pages = {248--260},
25
+ year = {2022},
26
+ editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},
27
+ volume = {174},
28
+ series = {Proceedings of Machine Learning Research},
29
+ month = {07--08 Apr},
30
+ publisher = {PMLR},
31
+ pdf = {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},
32
+ url = {https://proceedings.mlr.press/v174/pal22a.html},
33
+ abstract = {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset
34
+ designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS & NEET PG
35
+ entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token
36
+ length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other
37
+ options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across
38
+ a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above
39
+ information, is provided in this study.}
40
+ }
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  ```