Artificial Intelligence & Radiology - The Emerging Landscape

There is no question within healthcare, the sectors that is experiencing the most profound change is Radiology, with health tech platforms reading and interpreting skeletal and cardiovascular images, ophthalmology snapshots and determining the extent of a broken limb, damaged artery or diabetic retinopathy.

Indeed, there will be an on-going need for radiologists, but the healthcare system needs to find better and more efficient ways to read, classify and diagnose images.

Artificial intelligence had commenced in ernes within health care over the last two years, with the FDA approving a number of AI platforms in diagnosing pneumothorax (collapsed lung), breast abnormalities, partial cardiovascular imaging (left ventricular ejection fraction), diabetic retinopathy. Within China, various healthcare institutes and universities have made landmark health tech AI platforms in diagnosing lung cancer and interpreting various CT scans.

However, AI is not straightforward because one has to develop complex algorithms and systems to analyse the images and clinical notes and it’s a constant process of trial and error. AI does have limitations, but when combined with the skill of the Radiologist we will be able to analyse radiology images more accurately and efficiently and reduce the burden on the healthcare system.

Indeed, when liaising with Radiologists they maintain that company X has already devised an AI process to read various injuries and ailments, what’s the point of making another similar one and wasting wads of cash.

Our response is simple, if you do not at least change your mindset, explore your AI healthtech options and start building a MVP in image recognition and classification, your radiology business within 7 years may be on its last legs. Just look around to see how technology has transformed our lives and which companies are a distant memory.

Experience has also demonstrated that the first AI model will not be perfect, irrespective of uploading tens of thousands of clinical images and notes as learning sets. AI is also a mechanical process utilizing neural networks and the better we train the algorithms the better the end results. Indeed, it will take time to build an AI system for Radiology, but delaying the building process is the largest dilemma.

Hence, healthcare businesses need to be mindful of the changing healthtech environment and how their services will be funded. Governments are looking to find better value from their healthcare budgets, and if AI can improve the delivery of radiology services, then the old adage of the human eye can do it better will no longer work.

Finally, this is where Karni Healthtech can help, one of its founders, Stepan Tsaturyan, has a very in-depth experience and knowledge of artificial intelligence methodologies, from its programming framework, algorithm processing and irritation processes. Jake Boghossian also has very extensive stakeholders engagement skills and acute experience within the medical device area and business processes optimization. Feel free to contact Karni Healthtech for a face to face meeting and roadmap your AI tech process forward.  

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