Update README.md

README.md

@@ -123,7 +123,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 Gemma 4 E4B was chosen over MedGemma 1.5 4B ([arXiv:2604.05081v2](https://arxiv.org/abs/2604.05081v2)) for three reasons specific to this project's deployment requirements:
 
-- **Native thinking mode** — extended chain-of-thought reasoning allows the clinician to follow and evaluate the model's reasoning process, not just receive an opaque conclusion. MedGemma 1.5 4B (Gemma 3-based) does not have this capability regardless of fine-tuning.
+- **Thinking mode** — extended chain-of-thought reasoning allows the clinician to follow and evaluate the model's reasoning process, not just receive an opaque conclusion. MedGemma 1.5 4B activates thinking via a prompted system instruction appended at inference time — it is not natively integrated into the architecture. Gemma 4 E4B, by contrast, controls thinking via a dedicated <|think|> token built into the model from the ground up, making it a first-class architectural capability rather than a prompted behaviour.
 - **Mobile-first deployment** — the E4B model is purpose-built for efficient local execution on smartphones. MedGemma 1.5 4B makes no equivalent claim about mobile optimisation, and its expanded capabilities — processing 3D CT/MRI volumes of up to 85 axial slices (21,760 vision tokens) and whole slide pathology images of up to 126 patches (32,256 vision tokens) per query ([arXiv:2604.05081v2](https://arxiv.org/abs/2604.05081v2)) — might not be fully leveraged on a smartphone, especially in remote and isolated field settings. Google's own recommended production deployment path for MedGemma 1.5 4B points explicitly to cloud infrastructure: Model Garden and Google Cloud Storage, with specialised server-side processing for large medical images. Gemma 4 E4B, by contrast, was explicitly designed for efficient execution on everyday devices such as smartphones.
 - **No meaningful head start for this clinical scope** — MedGemma 1.5 4B's medical pre-training reflects hospital-grade diagnostics (chest X-ray, 3D radiology, whole slide pathology, dermoscopy, ophthalmology). Conditions such as cutaneous leishmaniasis, severe dengue, Chagas disease, and Oropouche fever in children are not present in that training distribution. Both models require targeted fine-tuning for this scope; given that, Gemma 4's newer architecture with native reasoning and mobile optimisation is the stronger foundation.