Recent Research suggests AI able to interpret medical images using deep learning algorithms. New medical breakthroughs in AI is worth mentioning. Researchers at IBM estimate that medical images currently account for at least 90 per cent of all medical data, making it the largest data source in the healthcare industry. This becomes an overwhelming amount on a human scale when you consider that radiologists in some hospital emergency rooms are presented with thousands of images daily, most of which are not analyzed properly. Due to the large facets of healthcare processes, it is not uncommon to create datasets with over 10,000 or more features even after dimensionality reduction.
Prof. Denniston and team searched several medical databases for all studies published between 1st of January 2012 and 6th of June 2019. The team published the results of their analysis in the journal The Lancet Digital Health.
The selection process yielded only 14 studies whose quality was high enough to include in the analysis. Prof. Denniston explains, “We reviewed over 20,500 articles, but less than 1% of these were sufficiently robust in their design and reporting that independent reviewers had high confidence in their claims.”
“What’s more, only 25 studies validated the AI models externally (using medical images from a different population), and just 14 studies compared the performance of AI and health professionals using the same test sample.”
Within that handful of high-quality studies, we found that deep learning could indeed detect diseases ranging from cancers to eye diseases as accurately as health professionals. But it’s important to note that AI did not substantially outperform human diagnosis.”
However, the healthcare professionals in these scenarios were not given additional patient information they would have in the real world which could steer their diagnosis.
Prof David Spiegelhalter, the chair of the Winton centre for risk and evidence communication at the University of Cambridge, said the field was awash with poor research.
“This excellent review demonstrates that the massive hype over AI in medicine obscures the lamentable quality of almost all evaluation studies,” he said.
“Deep learning can be a powerful and impressive technique, but clinicians and commissioners should be asking the crucial question: what does it actually add to clinical practice?”Prof David Spiegelhalter
Dr Raj Jena, an oncologist at Addenbrooke’s hospital in Cambridge who was not involved in the study, said deep learning systems would be important in the future, but stressed they needed robust real-world testing. He also said it was important to understand why such systems sometimes make the wrong assessment.
“If you are a deep learning algorithm, when you fail you can often fail in a very unpredictable and spectacular way,” he said.
Mostly writes on growing tech trends, events, and future of technologies. He has a keen interest in tech entrepreneurial infrastructures and startup ecosystem of Nepal. He believes in using latest technologies in problem-solving and regularly patrols the progress in solving SDGs in developing countries. He is also a licenced amateur radio operator and ETC volunteer for disaster communication .