Dental Clinical Assistant 20B

Name: dental-clinical-assistant-20b
Availability: InStock

Chat assistant for structured treatment planning and clinical decision support (SFT)

Open source dental clinical assistant for diagnosis, treatment planning, and point‑of‑care decision support.

Quickstart (LoRA adapter)

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base = "openai/gpt-oss-20b"
adapter = "Wildstash/dental-clinical-assistant-20b"

tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(base_model, adapter)

🏆 Awards

Winner: Most Useful Fine‑Tune (OpenAI Open Model Hackathon) — see Devpost: https://devpost.com/software/dental-assessment-gpt

Structured output

Differential diagnosis
Management plan
Antibiotics and dosing (if indicated)
Follow-up protocol

Quick guide (read this)

What it is: Chat assistant for structured treatment planning and clinical decision support (SFT).
What it covers: endodontics, periodontics, oral surgery, prosthodontics, ortho, pediatrics.
Why trust it: trained on 2,494 expert‑validated synthetic cases; guideline‑aligned.
How to use: provide patient context (age, vitals, symptoms, exam); ask for differential, management, abx, follow‑up.
Safety: HIPAA‑friendly (no real patient data); outputs assist, not replace, clinical judgment.

Dataset statistics

2,494 cases; multi‑specialty coverage; structured JSON (presentation → assessment → plan).
Source: Wildstash/dental-2.5k-instruct.

Key features

Comprehensive dental coverage; evidence‑based plans; guideline adherence; step‑wise reasoning.

Training details

Method: LoRA (PEFT), 4‑bit; base: 20B decoder.
Optimizations: grad checkpointing; mixed precision; multi‑GPU.

Expert validation

Practicing dentists graded sample cases; refined to improve plausibility and completeness.