Anusheh Chaudry · Machine learning engineer I build LLM agents and model-serving infrastructure. Much of my earlier ML work was in health.

Clinical AI agents

Why did this patient's treatment change?

An agent that explains why a patient's treatments changed, citing evidence from the record and the notes.

• The agent works through tools: the patient's timeline, detected treatment changes, the labs around each change, note search, and read-only SQL. Every citation is checked: the cited record must exist and belong to the patient, and quoted text must appear in the note.
• I adapted SLIDERS, an open-source method for extracting tables from documents, to clinical notes. That meant a treatment-change schema with certainty (performed, planned, considered, held), rules for things like copied-forward medication lists, and a fast profile that cut one run from 45 model calls and 265 seconds to 8 calls and 32 seconds.
• A combined method lets SLIDERS propose candidate changes and the agent verify the most important ones. Early tests cover only a few case reports and one synthetic patient, so they're hints, not results; human-labeled evaluation is next.
• Built on public data (the MIMIC-IV demo and PMC-Patients case reports) while access to a larger clinical dataset is pending.

LLM agents · Tool use · Clinical NLP · OMOP · Evaluation

Web app · Team of four

PocketCare

Explains medical bills and insurance plans in plain language.

• Pipeline: Google Document AI reads the PDF, a de-identification step (Microsoft Presidio plus custom recognizers for things like account numbers) strips personal details, then Gemini 1.5 Flash explains it with task-specific prompts.
• Limits: raw documents pass through Document AI for OCR before de-identification, and a user's follow-up questions are sent to the model as written.
• Django REST API with session auth and CSRF protection; Next.js and TypeScript front end; saved bills and analyses live in a per-user document library.

My part: Set up the Django backend and built the bill-breakdown feature end to end, from upload and document parsing to analysis, including the prompts behind the bill and insurance explanations.

Next.js · Django · Gemini · Document AI

• Code for PocketCare

LLM assistant for emergency medicine · Team of three

AssistEMT

An LLM assistant for EMTs; GPT-4 with retrieval scored 88.3% on a written EMT exam.

• Built two EMT-specific datasets (1,056 Q&A pairs from national EMS guidelines and 1,562 from an EMT textbook) and fine-tuned GPT-3.5 on combinations of them plus PubMedQA.
• Compared that with GPT-4 using prompt engineering, and GPT-4 with retrieval over EMT documents and web search.
• On a 120-question written EMT exam, GPT-4 with retrieval and prompting scored 88.3% and GPT-4 alone 87.5%. Fine-tuning GPT-3.5 on all three datasets raised it from 76.6% to 80.0%.
• Trained EMTs graded each model on clinical scenarios with the official rubric. GPT-4 with prompt engineering did best there (0.80 average), ahead of GPT-4 with retrieval (0.66) and fine-tuned GPT-3.5 (0.43).

My part: Fine-tuned the GPT-3.5 models and built our EMT datasets, including a 1,562-pair question-and-answer set drawn from an EMT textbook.

Fine-tuning · RAG · GPT-3.5 · GPT-4 · Evaluation

• Paper for AssistEMT