README: add Tiny Titan + Best Demo badge tags; sync radiologist quote with published blog

README.md

@@ -18,6 +18,8 @@ tags:
   - achievement:offbrand
   - achievement:sharing
   - achievement:fieldnotes
+  - achievement:tinytitan
+  - achievement:bestdemo
 ---
 
 # CXR Draft Auditor
@@ -61,6 +63,8 @@ On a held-out set of 273 images, scored with the production draft parser, presen
 - Off-Brand (achievement:offbrand): the app wears a custom "Reading Room / Clinical Light" interface with a light and dark toggle, not the stock Gradio look.
 - Sharing is Caring (achievement:sharing): I published a small open dataset of real audit traces from this Space (see Links).
 - Field Notes (achievement:fieldnotes): I wrote up what I learned, including an evaluation-integrity lesson, as a blog post (see Links).
+- Tiny Titan (achievement:tinytitan): every model the app runs is genuinely tiny; the fine-tuned MedGemma grounding model and NVIDIA Nemotron-3 Nano 4B are each 4B parameters, within the badge's 4B limit, with no larger model anywhere in the stack.
+- Best Demo (achievement:bestdemo): the submission is the full package; a custom Reading Room app, a demo video that walks through an audit end to end, and a social post (see Links).
 
 This Space runs on hosted ZeroGPU, not on a local machine, so I do not claim Off the Grid.
 
@@ -86,13 +90,13 @@ The example images shipped with the Space are open NIH ChestX-ray14 images (via
 
 This project grew out of a real need described by a pediatric radiologist I know, Alexey Amelin (https://vk.ru/xraydiag). Under real workload (high reading volume, fatigue, time pressure, night shifts) and with genuinely subtle findings, a second read is valuable, with a human always in the loop. He has since tried the tool himself; here, in his own words, is what he thinks:
 
-> When a colleague told me about this project, the idea landed on familiar ground right away. Every radiologist knows the value of a second look. The reading volume is high, the eyes tire toward the end of a long worklist, time is short — add night shifts and even an experienced specialist may not catch a detail immediately. This is no reproach to the profession; it is its everyday reality. And some findings are genuinely subtle: a small effusion, a hidden mass, a line or a tube, a faint change compared with a prior film. That is exactly why a second opinion, a second read of the image, is genuinely useful: it is a safety net for the radiologist and for the patient alike.
+> When a colleague told me about this project, the idea landed on familiar ground right away. Every radiologist knows the value of a second look. The reading volume is high, the eyes tire toward the end of a long worklist, time is short — add night shifts and even an experienced specialist may not catch a detail immediately. This is no reproach to the profession; it is its everyday reality. And some findings are genuinely subtle: the smallest pleural effusion; focal changes superimposed on dense tissue; abnormalities of the chest organs in patients with coexisting somatic disease; the early changes of a disseminating process. It is not always possible to ask a colleague for a second read, even when the need for one is obvious. Emerging radiograph-audit models are a safety net for the radiologist and the patient alike.
 >
 > The concept of the tool itself resonates with me. It does not make a diagnosis; it compares the doctor's draft impression against the image and highlights the disagreements: a finding present on the image but missed or denied in the text; a claim in the text that the image does not support; and, separately, it surfaces potentially urgent findings for a second look — all marked on the image so a person can look again. The decision always stays with the doctor. That is precisely the kind of helper I would value — a calm second pair of eyes, not a replacement for the specialist.
 >
-> At the same time, as a pediatric radiologist, I should be clear: for pediatric chest radiography, artificial intelligence is less validated than it is for adults. Large, high-quality, age-diverse pediatric image sets are scarce — children make up only a small share of publicly available medical imaging, and the major open chest-radiograph databases were collected from adults. Because of this, models trained mostly on adult data can show clinically meaningful age-related bias in children — for example, a noticeable rise in false-positive cardiomegaly in infants. So tools that grew out of adult data should not be assumed reliable by default: they need to be independently validated and recalibrated on pediatric data before there is any talk of using them in children.
+> At the same time, as a radiologist whose practice is mainly pediatric, I should be clear: for pediatric chest radiography, artificial intelligence is less validated than it is for adults. Large, high-quality, age-diverse pediatric image sets are scarce — children make up only a small share of publicly available medical imaging, and the major open chest-radiograph databases were collected from adults. Because of this, models trained mostly on adult data can show clinically meaningful age-related bias in children — for example, a noticeable rise in false-positive cardiomegaly and thymomegaly in infants. So tools that grew out of adult data should not be assumed reliable by default: they need to be independently validated and recalibrated on pediatric data before there is any talk of using them in children.
 >
-> And let me underline this separately: this is a research and educational quality-assurance tool, not a medical device and not a diagnostic instrument. The final word always rests with a qualified radiologist.
+> And let me underline this separately: this is a research and educational quality-assurance tool, not a medical device and not a diagnostic instrument. Imaging findings cannot be interpreted in isolation from a particular patient's clinical and laboratory picture and history. The final word always rests with a qualified radiologist.
 >
 > — Alexey Amelin (https://vk.ru/xraydiag), pediatric radiologist