Medical Analysis

1. Install LM Studio (local-first, HIPAA-compliant pathway) → download Qwen 2.5 32B Q6_K (24 GB) or Llama 3.3 70B Q4_K_M (40 GB).
2. For clinical note review: local RAG pipeline (AnythingLLM + LM Studio). Upload de-identified clinical notes → ask: "Summarize this patient's history, flag abnormal lab values, and list current medications with dosages."
3. For medical literature search: index PubMed PDFs locally → semantic search: "Latest treatment guidelines for refractory hypertension in patients with CKD stage 3."
4. For differential diagnosis assistance: describe symptoms + lab results → LLM suggests possible diagnoses with supporting evidence. Critical disclaimer: AI is a clinical decision SUPPORT tool, not a replacement for physician judgment.
5. For radiology report drafting: describe imaging findings → LLM generates a structured radiology report draft.
6. Critical: Medical AI MUST run locally for HIPAA compliance. Cloud AI services (ChatGPT, Claude) are NOT HIPAA-compliant without a BAA (Business Associate Agreement), and even with a BAA, many healthcare organizations prohibit cloud AI for patient data.
7. For specialized medical models: Med-PaLM (not open-weight), BioMistral, MedAlpaca, and OpenBioLLM exist. BioMistral 7B handles basic clinical Q&A but 70B+ general models outperform medical-specific small models on complex reasoning.

$300-400 genuinely cannot do reliable medical analysis. Medical AI demands: (a) 70B+ models to minimize dangerous hallucinations (misdiagnosis = patient harm), (b) strict HIPAA compliance (local-only deployment), (c) verifiable outputs with citations. A 7B model on 12 GB VRAM will hallucinate drug interactions, confuse disease presentations, and miss contraindications — the liability risk is existential. For medical AI on a budget: used RTX 3090 24 GB (~$900 build, see /hardware/rtx-3090) + 64 GB RAM. Below that, use manual clinical resources. $400 buys literature search and basic terminology lookup, not clinical decision support. Be honest with healthcare professionals about this limitation — overpromising AI capability in medicine is dangerous.

Dual RTX 3090 48 GB total (~$1,600, see /hardware/rtx-3090). Runs Llama 3.3 70B Q5_K_M — professional-grade clinical note analysis, literature synthesis, and decision support. For a small clinic or research lab: handles 100+ patient records/day for summarization, flagging, and literature cross-referencing. HIPAA-compliant when deployed with proper access controls (encrypted storage, audit logging, role-based access). Total build: ~$2,500-3,500. For hospital-scale deployment: 4-8 GPU server with Llama 70B + specialized medical embedding models for patient record similarity search. The cost of one preventable adverse event justifies the hardware. Medical AI is the highest-stakes local AI use case — data privacy and model accuracy are non-negotiable.

The mistake: Using ChatGPT/Claude to analyze patient data (labs, notes, imaging reports) because "it's convenient" and "everyone does it." Why it fails: This is a HIPAA violation with potential criminal penalties. Cloud AI providers process uploaded data — patient data sent to OpenAI/Anthropic is stored on their servers, potentially used for training (depending on tier), and accessible to their employees. HIPAA requires a signed BAA with every vendor that handles PHI. Consumer ChatGPT/Claude accounts DO NOT have BAAs. Even enterprise tiers with BAAs are prohibited by many hospital IT policies because the data still leaves the institution's control. Multiple healthcare organizations have fired employees for this. The fix: Use local-only AI for any workflow involving patient data. LM Studio + AnythingLLM + Llama 3.3 70B on an air-gapped or firewalled local server. If your institution can't afford local AI hardware, don't use AI on patient data — use traditional clinical resources. The convenience of cloud AI is not worth losing your medical license, facing HIPAA fines ($50K-1.5M per violation category), or harming a patient through a hallucinated analysis. Never put PHI into cloud AI. Ever.