Case Study
Train Smarter AI
Fuel your models with high-quality academic data.
Build datasets, fine-tune models, and create comprehensive knowledge bases.
The Specialist's Dilemma
General-purpose LLMs like ChatGPT or Google Gemini are powerful, but they often hallucinate when asked about niche subjects. Ask a generic model about "paraneoplastic pemphigus", a rare autoimmune disease, and it might invent a treatment protocol from thin air.
Suppose you want to build a specialized AI assistant for rare immunological disorders. You need to train it on the "long tail" of medical case reports, not just Wikipedia. The crucial step is moving beyond the open web to a trusted source of knowledge like academic publications. ScholarAPI makes this easy by giving you instant, programmable access to millions of papers through a simple REST interface.
While this case study focuses on medicine, the same workflow applies to materials science, legal tech, chemical engineering, and any domain requiring deep scientific precision.
Generic LLM
Paraneoplastic pemphigus is ...
a common skin condition caused by sun exposure. It typically presents as a mild rash on the face and arms. Treatment involves topical moisturizers and avoiding direct sunlight.Hallucinated content (!) based on probable word associations.
Specialist AI Model fine-tuned for Immunology
Paraneoplastic pemphigus is ...
a rare autoimmune mucocutaneous blistering disease often associated with lymphoproliferative neoplasms. It is characterized by severe painful stomatitis and polymorphous cutaneous eruptions. The condition is mediated by autoantibodies targeting plakin family proteins, specifically envoplakin and periplakin.Precise definition derived from fine-tuning on academic texts.
import requests
params = {
'q': ['"autoantibodies"', '"plakin proteins"', '"envoplakin"']
}
papers = []
while True:
resp = requests.get(
"https://scholarapi.net/api/v1/list",
params=params,
headers={"X-API-Key": "YOUR_KEY"}
)
results = resp.json().get('results')
if not results: break
for hit in results:
text = requests.get(
f"https://scholarapi.net/api/v1/text/{hit['id']}",
headers={"X-API-Key": "YOUR_KEY"}
).text
papers.append({**hit, 'full_text': text})
params['indexed_after'] = results[-1]['indexed_at']Gathering Knowledge
For effective training, you need a critical mass of domain-specific data.
To aggregate thousands of papers on immunology, biomarkers, and pathology,
use ScholarAPI's /list endpoint and pass specific terms like
"autoantibodies", "plakin proteins", or "envoplakin" via the q parameter.
Publications that contain one or more of the query phrases will be returned in indexing order,
in batches of up to 100 records.
Then, use the /text or /texts endpoint to download the plain text of each article. This process creates a dense, high-quality corpus of raw academic prose that reflects the true complexity of the field.
Generating the Dataset
Raw text isn't enough for instruction tuning; you need structured examples of the tasks you want the model to perform.
Use a helper LLM to scan your raw academic corpus and automatically generate thousands of training pairs across diverse categories:
- Summarization: Condense complex abstracts.
- Q&A: Create questions based on findings.
- Extraction: Pull out biomarkers and dosages.
- Clinical Reasoning: Simulate diagnostic logic.
This transforms passive reading material into active training drills.
Generation Pipeline
input)instruction, output) pairsThe Training Set
Samples generated from academic texts will teach the model
to generate the output when provided with an instruction (task description) and input (article chunk) as context.
Q&A
{
"instruction": "What can be the ocular manifestations
of paraneoplastic pemphigus?",
"input": "Ocular involvement is frequent and severe...
Conjunctivitis can lead to scarring...",
"output": "Severe conjunctivitis leading to
scarring and symblepharon is a hallmark..."
}Clinical Reasoning
{
"instruction": "Analyze the clinical significance
of the patient's elevated IgE levels",
"input": "Patient presents with... Lab results
show IgE > 2000 IU/mL...",
"output": "The markedly elevated IgE suggests
a hyper-IgE syndrome or severe atopic
dermatitis..."
}Extraction
{
"instruction": "Extract all biomarkers mentioned
in the input text",
"input": "The study analyzed serum levels of
IL-6, TNF-alpha, and CRP in 50 patients.",
"output": ["IL-6", "TNF-alpha", "CRP"]
}import requests
def fetch_multimodal_data(paper_id):
# Get the full PDF binary
resp = requests.get(
f"https://scholarapi.net/api/v1/pdf/{paper_id}",
headers={"X-API-Key": "YOUR_KEY"}
)
with open(f"{paper_id}.pdf", "wb") as f:
f.write(resp.content)
# Pass to extraction pipeline (e.g. PyMuPDF, Unstructured)
# extract_images_and_captions(f.name)Multi-Modal Enrichment
Medical diagnosis is visual. Text alone isn't enough when the evidence lies in histology slides, X-rays, and flow cytometry charts.
ScholarAPI's /pdf endpoint gives you access to the full binary documents. By downloading the complete PDFs, you can extract images and their associated captions to enrich your dataset and train multi-modal models that can "see" medical imagery as well as read the clinical notes.
Supervised Fine-Tuning
With your generated instruction dataset and, possibly, the extracted multi-modal features, you are ready for Supervised Fine-Tuning (SFT).
Feed the prepared instruction / output pairs into a pretrained base model like Llama or Mistral.
Over thousands of training steps, the model adapts its weights to the specialized vocabulary and reasoning patterns of immunology.
The result is an expert model that no longer guesses but genuinely knows the domain.
from transformers import AutoModelForCausalLM, TrainingArguments
from trl import SFTTrainer
# Load your domain-specific dataset
dataset = load_dataset("json", data_files="immunology_tasks.jsonl")
# Configure Low-Rank Adaptation (LoRA) for efficiency
peft_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"]
)
# Initialize the trainer
trainer = SFTTrainer(
model="mistralai/Mistral-7B-v0.3",
train_dataset=dataset,
peft_config=peft_config,
args=TrainingArguments(
output_dir="./immunogen-v1",
num_train_epochs=3,
per_device_train_batch_size=4
)
)
trainer.train()What are the latest 2026 findings on "CAR-T safety"?
"According to 2026 studies, CAR-T safety has improved with new cytokine management protocols..."
RAG for Real-Time Precision
Training cuts off at a certain date, but medicine evolves daily. If your model needs to know about the latest trials and protocols, extend model inference with Retrieval-Augmented Generation (RAG).
When a clinician asks a question, your system identifies the important keywords and queries ScholarAPI in real-time to find the most relevant fresh papers. These are injected as raw text into the model's context window, allowing it to answer with up-to-the-minute accuracy.
This hybrid approach—deep domain knowledge from SFT plus fresh facts from RAG—creates an AI that is both wise and current.