Building Healthcare Tools with LLMs

#aiinfrastructure #oxlo #ai

Healthcare generates the longest unstructured documents of any industry. A single patient record can span hundreds of pages of clinical notes, discharge summaries, and prior authorization histories. Large language models promise to extract insights from this data, but token-based inference costs scale directly with input length. For production healthcare tools, that cost model creates unpredictable budgets and discourages deep context analysis. Oxlo.ai offers a fundamentally different approach: flat per-request pricing that stays constant whether you send a one-line prompt or a complete electronic health record.

The Clinical Context Window Problem

Clinical NLP tasks require reading entire patient histories, not just isolated snippets. Summarizing a hospital stay, reconciling medication lists across years of notes, or preparing a case for peer review all require models that accept long contexts. On token-based platforms, each additional page of input raises the bill. Oxlo.ai uses request-based pricing, so one flat cost covers the full API call regardless of prompt length. Compared to token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale, this architecture can be significantly cheaper for long-context workloads.

Oxlo.ai hosts several models suited to these clinical loads. DeepSeek V4 Flash supports a 1M token context window for efficient long-document analysis. Kimi K2.6 offers advanced reasoning and a 131K context, while Llama 3.3 70B serves as a general-purpose flagship for reliable summarization. Qwen 3 32B adds multilingual reasoning for patient populations requiring documentation in multiple languages. All run with no cold starts, so clinical tools respond immediately under load.

Structured Extraction and FHIR Interoperability

Healthcare applications rarely want raw prose. They need structured data that integrates with EHRs, billing systems, and research databases. Oxlo.ai supports JSON mode and function calling through a fully OpenAI-compatible API, making it straightforward to extract FHIR-compliant objects or custom schemas from clinical text.

The following example uses the OpenAI Python SDK to point at Oxlo.ai and extract medication statements from a discharge summary:

import openai
import json

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

schema = {
    "type": "object",
    "properties": {
        "medications": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "dosage": {"type": "string"},
                    "route": {"type": "string"},
                    "frequency": {"type": "string"}
                },
                "required": ["name", "dosage", "route", "frequency"]
            }
        }
    },
    "required": ["medications"]
}

response = client.chat.completions.create(
    model="llama-3.3-70b",  # example model ID
    response_format={"type": "json_object"},
    messages=[
        {
            "role": "system",
            "content": "Extract all medications from the clinical text and return valid JSON matching the provided schema."
        },
        {
            "role": "user",
            "content": "Patient discharged on metformin 500 mg by mouth twice daily, and lisinopril 10 mg by mouth once daily."
        }
    ]
)

data = json.loads(response.choices[0].message.content)
print(json.dumps(data, indent=2))

Because Oxlo.ai charges per request, extracting structured data from a 10,000-token note costs the same as extracting from a 100-token snippet. This predictability lets engineering teams build batch ETL pipelines over entire health systems without token-counting guardrails.

Agentic Workflows for Prior Authorization

Prior authorization and clinical decision support are inherently agentic. A model must read a request, query a formulary, check contraindications, and draft a determination letter, often across multiple turns and tool calls. Oxlo.ai supports multi-turn conversations, function calling, and streaming responses, so you can build agents that iterate without incurring runaway token costs.

For deep reasoning steps, DeepSeek R1 671B MoE and Kimi K2.5 provide advanced chain-of-thought capabilities. GLM 5, a 744B MoE model, handles long-horizon agentic tasks that require planning across many steps. Minimax M2.5 and DeepSeek V3.2 are strong options for coding and tool use when the agent must generate SQL or API calls to interact with a clinical database.

With request-based pricing, an agent that sends five long-context requests to Oxlo.ai is billed for five requests. On token-based platforms, the same agent might accumulate unpredictable costs as it passes full patient histories back and forth through each reasoning loop.

Multimodal Analysis for Medical Imaging

Text is only part of the clinical picture. Radiology reports, dermatology screenings, and retinal scans increasingly require vision-language models. Oxlo.ai offers vision capabilities through chat/completions endpoints that accept image input. Kimi K2.6 supports vision alongside advanced reasoning and agentic coding, while Gemma 3 27B provides a capable open-source vision option.

Using the same OpenAI SDK pattern, you can pass base64-encoded imaging alongside a prompt:

response = client.chat.completions.create(
    model="kimi-k2.6",  # example model ID
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe any abnormalities visible in this chest X-ray."},
                {"type": "image_url", "image_url": {"url": "data:image/png;base64,iVBORw0KGgo..."}}
            ]
        }
    ]
)

Vision requests are also billed per request, not by image tokens or pixels, keeping computer-vision pipelines as predictable as text-only workloads.

Cost Predictability at Scale

Healthcare IT procurement demands fixed budgets. Token-based pricing forces teams to estimate average input lengths, buffer for outliers, and throttle long-context queries to avoid spikes. Oxlo.ai replaces that uncertainty with flat per-request pricing. For long-context and agentic workloads, request-based pricing can be 10-100x cheaper than token-based alternatives.

Oxlo.ai offers several plans to match deployment stages. The Free plan provides $0 per month, 60 requests per day, and access to more than 16 models, including a 7-day full-access trial. The Pro plan at $80 per month includes 1,000 requests per day across all models. The Premium plan at $350 per month raises the limit to 5,000 requests per day with priority queue access. Enterprise contracts add dedicated GPUs and unlimited volume. Exact details are available at https://oxlo.ai/pricing.

Getting Started with Oxlo.ai

Oxlo.ai is a drop-in replacement for the OpenAI SDK. Change the base URL to https://api.oxlo.ai/v1, swap in your Oxlo.ai API key, and all existing code for chat completions, embeddings, image generation, audio transcription, and speech synthesis continues to work. The platform hosts more than 45 open-source and proprietary models across seven categories, from LLMs and code models to embeddings and object detection, so a single API key can power an entire healthcare AI stack.

For teams building clinical summarization, FHIR extraction, prior authorization agents, or multimodal diagnostics, Oxlo.ai provides the long-context capacity and pricing predictability that healthcare production environments require.