README.md

metadata

license: cc-by-4.0
task_categories:
  - question-answering
language:
  - en
tags:
  - health
  - medical
  - africa
size_categories:
  - 10K<n<100K

AfriMed-QA v2: A pan-African Medical QA Dataset

🏆 Best Social Impact Paper Award — ACL 2025 (Vienna, Austria, Association for Computational Linguistics)

This work is licensed under a Creative Commons Attribution 4.0 International License.

Project Website: AfriMedQA.com

Paper URL: https://aclanthology.org/2025.acl-long.96/

Collaborating Organizations: Intron Health, SisonkeBiotik, BioRAMP, Georgia Institute of Technology, MasakhaneNLP, Google Research

Funded by: Google Research, Bill & Melinda Gates Foundation, PATH,

Summary

AfriMed-QA creates a novel multispecialty open-source dataset of 15,000 pan-African clinically diverse multiple choice and open ended questions and answers to rigorously evaluate LLMs in African healthcare. Sourced from over 600 clinical and non-clinical contriubtors across 16 countries and covering 32 clinical specialties, the dataset’s geographical and clinical diversity facilitates robust contextual evaluation of LLMs for accuracy, factuality, hallucination, demographic bias, potential for harm, comprehension, and recall, and provides a sufficiently large corpus to finetune LLMs to mitigate biases discovered.

Background

The meteoric rise of LLMs and their rapid adoption in healthcare has driven healthcare leaders in developed economies to design and implement robust evaluation studies to better understand their strengths and weaknesses. Despite their stellar performance across multiple task domains, LLMs are known to hallucinate, propagate biases in their training data, spill potentially harmful information, and are prone to misuse. Since a large proportion of LLM training data are sourced from predominantly western web text, the resulting LLMs have limited exposure to LMIC-specific knowledge. Furthermore, there is minimal evidence to show that stellar performance touted in western literature transfers to healthcare practice in developing countries given their underlying knowledge gap. As with medical devices, drugs, and other interventions in healthcare, physician and patient exposure to health AI tools should be evidence-based, a result of rigorous evaluation and clinical validation to understand LLM strengths and weaknesses in context.

This is the first and largest effort to create a pan-African multi-region multi-institution dataset addressing multiple axes of LLM capabilities, rigorously documenting evidence in the context of African healthcare highlighting use cases or clinical specialties where LLMs shine as well as situations where they fall short or have a high potential for harm.

The project will be a timely and invaluable resource guiding the African academic, clinical, biomedical, and research communities on the utility of LLMs in African healthcare at a scale that not only enables robust and rigorous LLM evaluation but provides a sufficiently large dataset to mitigate biases discovered– by finetuning these LLMs, adequately exposing model weights to African healthcare data in context. Such a rigorous evaluation could also uncover desirable and highly valuable but unexplored applications of LLMs in African healthcare, enabling African healthcare professionals to use LLMs in novel and relevant ways that improve patient outcomes.

Dataset Stats [Version 2 release]

• Number of Questions: 15,275
• Total Number of Unique Contributors: 621
• Countries: 16 ['NG', 'TZ', 'KE', 'GH', 'UG', 'BW', 'PH', 'ZA', 'ZW', 'LS', 'ZM', 'MZ', 'AU', 'SZ', 'US', 'MW']
• Medical Specialties: 32+ (Allergy and Immunology, Anesthesiology, Cardiology, Preventive & Community Health, Dermatology, Emergency Medicine, Endocrinology, Family Medicine, Gastroenterology, General Surgery, Geriatrics, Hematology, Infectious Disease, Internal Medicine, Medical Genetics, Nephrology, Neurology, Neurosurgery, Obstetrics and Gynecology, Oncology, Ophthalmology, Orthopedic Surgery, Otolaryngology, Pathology, Pediatrics, Physical Medicine and Rehabilitation, Plastic Surgery, Psychiatry, Pulmonary Medicine, Radiology, Rheumatology, Urology)
• Medical Schools: 60+
• Gender: Female 55.56% / Male 44.44.35%

Question Type

	tier	num questions
AfriMed-QA-Consumer-Queries	Crowdsourced	10,000
AfriMed-QA-Expert-MCQ	Experts	3,910
AfriMed-QA-Expert-SAQ	Experts	359
AfriMed-QA-SAQ	Crowdsourced	877
AfriMed-QA-MCQ	Crowdsourced	129

• Crowdsourced = 11,006
• Expert = 4,269
• Total SAQ = 1,236
• Total MCQ = 4,039
• Total CQ = 10,000

Specialties

specialty	# Questions
Obstetrics_and_Gynecology	1008
General_Surgery	757
Pediatrics	747
Infectious_Disease	539
Pathology	381
Neurology	310
Psychiatry	299
Family_Medicine	289
Cardiology	258
Internal_Medicine	238
Endocrinology	236
Pulmonary_Medicine	231
Gastroenterology	225
Allergy_and_Immunology	217
Hematology	211
Ophthalmology	202
Rheumatology	171
Nephrology	163
Orthopedic_Surgery	161
Otolaryngology	158
Oncology	135
Urology	134
Plastic_Surgery	132
Dermatology	126
Other	125
Emergency_Medicine	123
Anesthesiology	115
Neurosurgery	107
Radiology	107
Physical_Medicine_and_Rehabilitation	101
Geriatrics	91
Medical_Genetics	86

How to Use the Data

AfriMed-QA-MCQ [Multiple Choice Questions]: These are multiple choice questions where 2 - 5 answer options are provided with at least one correct answer. Each question includes the correct answers(s) along with an explanation or rationale. Data columns Required : question, answer_options, correct_answer, answer_rationale. Please note that there are 3 types of MCQ questions. 1) True/False where only 2 answer options are provided, 2) Single correct answer, and 3) Multiple correct answers

AfriMed-QA-SAQ [Short Answer Questions]: These are open-ended questions that require a short essay, usually one to three paragraphs. Answers must include context, rationale, or explanations. Data columns Required : question, answer_rationale. Evaluate model performance based on overlap with human answer/rationale

AfriMed-QA-Consumer-Queries: These represent questions provided by contributors in response to a prompt or clinical scenario. For example, the prompt could be, "Your friend felt feverish and lightheaded and feels she has Malaria. What questions should she ask her doctor?". The contributor could then respond by asking, "How many days should I wait to be sure of my symptoms before seeing the doctor". A clinician contributor could then respond with an answer along with the rationale. Data columns Required : prompt, question, answer_rationale. You will need to concatenate the prompt with the question for full context.

from datasets import load_dataset

afrimedqa = load_dataset("intronhealth/afrimedqa_v2")

Data Description

The dataset (csv) contains a few important columns:

• sample id: unique record identifier
• question_type: Multiple Choice (MCQ), Short Answer (SAQ), or Consumer Queries
• prompt: the clinical scenario on which the contributor will base their question. This field is valid only for consumer queries
• question: the human-generated question
• question_clean: post-processed question text to fix issues with new lines and spacing around numbers, units, and punctuations
• answer_options: 2 to 5 possible answers for MCQs. Only valid for MCQs
• correct_answer: the correct answer(s)
• answer_rationale: explanation or rationale for selected correct answer
• specialty: indicates question (sub)specialty
• tier: crowdsourced or expert-generated

Other fields provide more context on the contributor's (self-reported) background:

• gender: Female/Male/Other
• country: 2-letter contributor country code
• discipline: healthcare related or not, e.g. Nursing, Pharmacist, etc.
• clinical_experience: for contributors with healthcare backgrounds, this indicates if they are a student/trainee , resident, attending, etc.

Other fields report reviewer ratings of contributor questions/answers:

• quality: boolean thumbs up or down indicating reviwer opinion of question or answer quality or formatting issues
• neg_percent: a measure of how much we can rely on responses provided by the contributor. It is the percentage of thumbs down ratings the contributor has received out of all responses reviewed for the contributor on the project.

The following are 5-point scale ratings by reviewers on the following criteria:

• rated_african: Requires African local expertise
• rated_correct: Correct and consistent with scientific consensus
• rated_omission: Omission of relevant info
• rated_hallucination: Includes irrelevant, wrong, or extraneous information
• rated_reasonable: Evidence of correct reasoning or logic, even if the answer is wrong
• rated_bias: Indication of demographic bias
• rated_harmful: Possibility of harm

Columns for Bias Detection or Counterfactual Analysis, Boolean

• region_specific: contributor indicates if question requires local African medical expertise
• mentions_Africa: question mentions an African country
• mentions_age: question refers to patient age
• mentions_age: question mentions patient gender

The split column indicates samples assigned to train/test split. LLM responses to questions in the test split will be sent for human evaluation.

Data Quality Issues

This dataset was crowdsourced from clinician experts and non-clinicians across Africa. Although the dataset has gone through rigorous review to identify quality issues before release, it is possible that some issues may have been missed by our review team. Please report any issues found by raising an issue, or send an email to afrimed-qa-leadership@googlegroups.com.

Due to a bug in the early phase of developing the web application for collecting questions, when saving entries, new lines were ignored for a subset of questions. This predominantly impacts questions that list lab results. This bug was eventually found and fixed. Text for questions impacted were fixed by adding a space to separate concatenated text.

For example, in the following text where new lines are missing between each lab result:

Laboratory studies show:Hematocrit 42%Leukocyte count 16,000/mm3Segmented neutrophils 85%Lymphocytes 15% Platelet count 200,000/mm3Arterial blood gas analysis on an FIO2 of 1.0 shows:pH 7.35PCO2 42 mm HgPO2 55 mm HgChest x-ray shows diffuse alveolar infiltrates bilaterally and no cardiomegaly.

was transformed to

Laboratory studies show: Hematocrit 42% Leukocyte count 16,000/mm3 Segmented neutrophils 85% Lymphocytes 15% Platelet
 count 200,000/mm3 Arterial blood gas analysis on an FIO2 of 1.0 shows: pH 7.35 PCO2 42 mmHg PO2 55 mmHg Chest x-ray
 shows diffuse alveolar infiltrates bilaterally and no cardiomegaly.

These transformation result was stored in the question_clean column in the dataset.

Correspondence, Funding, and Commercial License Requests

Please send an email to: afrimed-qa-leadership@googlegroups.com

Or visit www.afrimedqa.com

BibTeX entry and citation info.

If you use this dataset, please cite:

🏆 This work received the Best Social Impact Paper Award at the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), Vienna, Austria.

@inproceedings{nimo-etal-2025-afrimed,
    title = "{A}fri{M}ed-{QA}: A Pan-{A}frican, Multi-Specialty, Medical Question-Answering Benchmark Dataset",
    author = {Nimo, Charles  and
      Olatunji, Tobi  and
      Owodunni, Abraham Toluwase  and
      Abdullahi, Tassallah  and
      Ayodele, Emmanuel  and
      Sanni, Mardhiyah  and
      Aka, Ezinwanne C.  and
      Omofoye, Folafunmi  and
      Yuehgoh, Foutse  and
      Faniran, Timothy  and
      Dossou, Bonaventure F. P.  and
      Yekini, Moshood O.  and
      Kemp, Jonas  and
      Heller, Katherine A  and
      Omeke, Jude Chidubem  and
      Md, Chidi Asuzu  and
      Etori, Naome A  and
      Ndiaye, A{\"i}m{\'e}rou  and
      Okoh, Ifeoma  and
      Ocansey, Evans Doe  and
      Kinara, Wendy  and
      Best, Michael L.  and
      Essa, Irfan  and
      Moore, Stephen Edward  and
      Fourie, Chris  and
      Asiedu, Mercy Nyamewaa},
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.96/",
    doi = "10.18653/v1/2025.acl-long.96",
    pages = "1948--1973",
    ISBN = "979-8-89176-251-0",
    abstract = "Recent advancements in large language model (LLM) performance on medical multiplechoice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-andmiddle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healthcare access and reduce costs. However, their effectiveness in the Global South, especially across the African continent, remains to be established. In this work, we introduce AfriMed-QA , the first largescale Pan-African English multi-specialty medical Question-Answering (QA) dataset, 15,000 questions (open and closed-ended) sourced from over 60 medical schools across 16 countries, covering 32 medical specialties. We further evaluate 30 LLMs across multiple axes including correctness and demographic bias. Our findings show significant performance variation across specialties and geographies, MCQ performance clearly lags USMLE (MedQA). We find that biomedical LLMs underperform general models and smaller edge-friendly LLMs struggle to achieve a passing score. Interestingly, human evaluations show a consistent consumer preference for LLM answers and explanations when compared with clinician answers."
}