Healthcare generates roughly 30% of the world's data volume, yet most of it remains unstructured across EHRs, clinical notes, imaging archives, and research literature. Large language models are now being deployed to extract meaning from this noise, transforming everything from clinical documentation to diagnostic workflows. For engineering teams building these systems, the challenge is not finding a capable model, but selecting inference infrastructure that remains predictable and cost-effective when prompts expand to include entire patient histories or multimodal imaging reports.
Clinical Documentation and Summarization
Clinical notes, discharge summaries, and specialist referrals often run to tens of thousands of tokens. Feeding an entire ICU stay or a longitudinal patient record into an LLM for summarization or timeline extraction quickly becomes expensive under token-based billing, where input length directly determines cost. Oxlo.ai uses request-based pricing, so a query that includes a 100,000-token record costs the same as a 1,000-token question. Models such as Llama 3.3 70B and Kimi K2.6, which offers a 131K context window, can ingest these long-form documents without aggressive truncation, preserving clinical nuance.
Medical Coding and Structured Extraction
Accurate ICD-10 and CPT coding from unstructured clinical text is a high-stakes automation target. LLMs can extract diagnoses, procedures, and medication lists, but healthcare systems require machine-readable output. Oxlo.ai supports JSON mode and function calling through the standard chat/completions endpoint, letting you constrain model outputs to strict schemas for downstream EMR ingestion.
from openai import OpenAI
import json
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "Extract medical codes from the note. Return JSON with keys: diagnoses, procedures, medications."},
{"role": "user", "content": "Patient admitted for laparoscopic cholecystectomy. History of type 2 diabetes mellitus on metformin..."}
],
response_format={"type": "json_object"}
)
structured = json.loads(response.choices[0].message.content)
print(structured)
Diagnostic Reasoning and Clinical Decision Support
Diagnostic assistance requires more than pattern matching; it demands extended reasoning across ambiguous symptoms, lab values, and imaging impressions. Chain-of-thought models such as DeepSeek R1 671B MoE, Kimi K2 Thinking, and GLM 5 generate detailed reasoning traces before arriving at conclusions. On token-based platforms, these internal reasoning tokens inflate costs. Because Oxlo.ai charges per request rather than per token, the cost of a complex differential diagnosis remains flat even when the model writes out a lengthy deliberation. This predictability makes it feasible to run agentic workflows that iterate over patient data without budget surprises.
Medical Imaging and Multimodal Analysis
Vision-capable LLMs are increasingly used to draft preliminary radiology reports or flag visible abnormalities in dermatology and ophthalmology scans. Oxlo.ai offers vision models including Kimi K2.6 and Gemma 3 27B through the same chat/completions endpoint, accepting base64-encoded images alongside text prompts.
import base64
def encode_image(path):
with open(path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
b64_image = encode_image("chest_xray.png")
response = client.chat.completions.create(
model="kimi-k2-6",
messages=[
{"role": "system", "content": "You are a radiology assistant. List findings in plain language."},
{"role": "user", "content": [
{"type": "text", "text": "Describe any abnormalities visible in this chest X-ray."},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64_image}"}}
]}
]
)
print(response.choices[0].message.content)
Research Synthesis and Drug Discovery
Pharmaceutical and clinical research teams use LLMs to synthesize findings across thousands of papers, trial protocols, and patent filings. DeepSeek V4 Flash supports up to 1M tokens of context, enabling researchers to pass entire document sets in a single request rather than chunking and losing cross-paper dependencies. GLM 5, a 744B parameter MoE, is designed for long-horizon agentic tasks such as hypothesis generation and multi-step literature review. For these workloads, request-based pricing is particularly effective, because a single request can replace dozens of smaller, chained calls that would each incur separate token charges elsewhere.
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