Dr. Ameya Kawthalkar

Consultant MSK Radiologist @MWLNHS | Fellowship in MSK Radiology@ SMK Netherlands | MSK Radiologist @ Paris Olympics 2024

[Dr. Ameya Kawthalkar @ASK_MSK]

@bhavinj @CafeRoentgen Do yourself a favour in 2026 and get off as many platforms as you can.

[Dr. Ameya Kawthalkar @ASK_MSK]

Getting off algorithm driven social media is the best!
Been 2 months since deleting X and Linkedin from my phone, with no social media use except WhatsApp and Telegram. Logging in through my browser just to post this, that friction of not having the app on your phone is necessary

[Dr. Ameya Kawthalkar @ASK_MSK]

@bhavinj @CafeRoentgen Is letting silicon valley zombify your brain worth the occasional academic radiology post ?
The generative AI fiasco proves silicon valley is not on your side, they just want people addicted and hooked to their apps, whether chatbots or social media.

[Journal of Neurosurgery @TheJNS]

#OnlineFirst: Unraveling the cause of microspurs in spontaneous intracranial hypotension type 1: discogenic origin or calcified Hofmann’s ligament?

https://t.co/ucN57ZVorE

[British Journal of Sports Medicine (BJSM) @BJSM_BMJ]

⚠️ T-junction injuries of the biceps femoris

NEW #Editorial bridging the gap between expert opinion and evidence-based practice 📄 ✅

Read ➡️ https://t.co/XH42SCEaCk

[James Noake @DrJN_SportsMed]

Consent ✅

Footballer - ACL recon & MCL tear 12 months ago

Uneventful rehab process & RTP ...... but then developed focal pain over medial joint line & restricted flexion

Sore on side passing & cutting / change of direction drills

OE -

Palpation pain at proximal MCL
Stiff & painful into EROM flexion

MRI - no medial pathology reported - but on closer inspection, subtle medium signal focal change in deep MCL on T1 & T2 (always check the scan yourself)

POCUS - obvious large heterotopic calcific deposit in previously injured proximal MCL fibres - Pelligrini-Stieda lesion

Video - needle barbotage / fenestration of deposit, finished with a soupçon of CSI 🫰

Post procedure, complete abolition of pain in gym & typical provocative movements ✅

Ultrasound is much better than MRI in identifying calcific pathology (as is plain x-ray)

[Dr. Ameya Kawthalkar @ASK_MSK]

@kafkaesque2311 @REF_INDIA I have quite low reach, this post has likely not reached the AI cultists here.

[Dr. Ameya Kawthalkar @ASK_MSK]

My article "Should I have become a radiologist? The hype versus reality of radiology AI, AI in general and the road ahead" .

It is based on my recent talk for @REF_INDIA with a few additions.

It is intended for medical students considering a career in radiology but facing a barrage of “AI will replace radiologists” fear mongering and for radiologists and radiology trainees seeking a distilled overview of the state of radiology AI today and what to expect going forward.

Link in 🧵

[Dr. Ameya Kawthalkar @ASK_MSK]

Article link : https://t.co/ZvB6Arhldi

[Dr. Ameya Kawthalkar @ASK_MSK]

@DrDatta_AIIMS Very well written, very few people qualified enough like @DrDatta_AIIMS to speak with clarity on this !

[Dr. Ameya Kawthalkar @ASK_MSK]

@DrDatta_AIIMS Long term very optimistic about beneficial effects of AI. Short term this is a crazy financial bubble driven by big AI companies, could turn out deep learning itself is not optimal architecture for healthcare as it's a black box, we might need something completely different.

[Dr. Ameya Kawthalkar @ASK_MSK]

I hope AI slop takes traditional social media down with it, the American AI companies are not even hiding their intentions anymore.

Probably doing own website + newsletter like @bhavinj and @Dr_AkshayBaheti makes sense, or telegram channels like @atultanejamd and Kushal Gupta. Getting people away from the dopamine fuelled infinite scroll and likes, reposts model.

[Dr. Ameya Kawthalkar @ASK_MSK]

Came across this AI generated 'educational' radiology post which is just plain wrong but has thousands of likes across X and Insta.

We are living through the death of traditional social media, hastened by AI slop which is now apparently coming for medical educational posts.

Radiology creators like @drvenkimdrd
@drdevrad @RadiologyVibes @teachplaygrub
@bhavinj @msk_munoz @GSERRANOB_MSK @drmankad and countless others across different social media platforms put in hours to make sure verified, high quality radiology education reaches people.
Do all those who have clicked like on this post know what's correct and what's not here ? Are all those clicking like even human, and what percentage are bots ?

Who is responsible when AI generated false medical educational material (not necessarily obvious as in this case but subtle) has a direct adverse impact on patients?

There is a need for something new now, a "post-social media" platform which is not algorithm driven for maximising engagement and likes, and which is not AI slop filled. Towards something where only human creators and their creations are valued.

[Dr. Ameya Kawthalkar @ASK_MSK]

@drvenkimdrd @drsthanus This is the death of the internet here if an AI generated radiology post which is not even accurate is getting thousands of likes across platforms. Creators like you , @drdevrad , @RadiologyVibes need to speak out

[sharad maheshwari MD @abdo_imaging]

A new code of AI is born: https://t.co/8oB1YAiaRc
Don’t just code! Code responsibly!!

AI ideas are launched fast- few are launched responsibly #BeResponsibleAI changes that its a #movement. It’s the world’s first Responsible AI chat that evaluates your AI idea across 5 pillars.

[Dr. Datta M.D. (Radiology) M.B.B.S. 🇮🇳 @DrDatta_AIIMS]

🚨 Just published! All frontier AI models have failed “Radiology’s Last Exam” - the toughest benchmark in radiology launched today!

✅ Board-certified radiologists scored 83%, trainees 45%, but the best performing AI from frontier labs, GPT-5, managed only 30%.

❌ These results shatter repeated claims of “doctor-level” AI in medicine and give you a reality check!

🇮🇳 The Centre for Responsible Autonomous Systems in Healthcare (#CRASHLab), @KCDH_A @AshokaUniv, India has launched v1 of one of the hardest benchmarks in medicine and we share our results with the world!

1/n

[Dr. Ameya Kawthalkar @ASK_MSK]

@karpathy We are entering a golden era for global teleradiology for those radiologists with the required skill sets (and certifications). No AI paradigm available today can replace radiologists, and regulations keep radiology jobs safe.

https://t.co/rSOQrdTuW6

[Dr. Ameya Kawthalkar @ASK_MSK]

I have been testing Google's new image model nano banana and it is not working with medical images. It fails to annotate even simple structures on chest x-rays. The same with GPT5.

This highlights a fundamental issue in AI for medical imaging and allow me to cut through the hype of "AI will soon replace radiologists." Those who have trained radiology AI models AND worked in a clinical setting understand this well. A very basic hurdle is the lack of adequate medical imaging data available to train these models. There are no adequate datasets online for most radiology reporting purposes, at the sizes required to train foundation models. You need a minimum of 5000 to 10,000 scans+reports for every single pathology which can be seen on that scan to adequately train an AI model. And for every single anatomical structure on that scan (they are in the hundreds), there are countless things which can go wrong. Neither anatomy nor disease reads textbooks and there are numerous variations of anatomy and diseases on imaging.

This is multiplied 100x in histopathology, where the specialty is not even digitalized in most places worldwide so there is even scarce data for AI training. Histopathology image sizes could be in GBs and tissue variations are even larger and wilder for the same disease than for radiology.

AI models trained on medical images from a certain scanner, patient demographic or country fail on images from different ones. Even an AI model trained on medical images from Liverpool will fail to perform well just a half hour drive away in a hospital in Manchester. PubMed and Radiopaedia are not enough at all to train an AI model to report in any clinical setting.

This is on top of AI foundation model training being GPU heavy and requiring thousands of dollars to train one to be of any clinical use.

Today with agentic workflows and RAG for vision language models (maybe with access to Radiopaedia and local reporting guidelines) it is possible to achieve an accuracy boost for radiology reporting, but still nowhere near as good to be clinically deployable.

Big AI today is focused on optimizing LLMs for coding, in the hope of creating a superhuman coding model which writes its own code and recursively self improves, leading to a superintelligence. Such a superintelligence could theoretically be able to design new AI paradigms which do not require thousands of scans of degenerative lumbar spine MRs for training it, and could function well with few hundreds of them or even less. Removing AI bias might be possible, or not. Maybe we embrace bias in AI and fine tune AI models in each radiology department locally. Then again most hospitals worldwide don't have GPUs which each cost thousands of dollars, and are essential to train and run most AI models today. And most don't have the funds to invest in them for experimenting with new technologies which may or may not work.

The way AI has evolved has led it to master language and code first. This has a direct impact on language based medical specialties whose work will be significantly augmented and improved by LLMs like all physician non-surgical, non medical imaging specialties such as GPs, cardiology, critical care, paediatrics and others. Until LLMs hallucinate or make errors they will not be given regulatory approvals to work in clinical settings independent of doctors. So till then they will not replace a single doctor. Same goes for medical imaging models.

Whoever solves the fundamental roadblock of lack of diverse open access medical imaging datasets will accelerate medical imaging AI. Till then radiologists and pathologists are as safe or even safer than many other professionals, in medicine and in general.

You are welcome to forward the above post to medical students considering a career in radiology or pathology and worried about 'AI replacement'.