Artificial Intelligence (AI) is redefining precision diagnostics and early detection of multiple diseases, including cancer, neuro-critical conditions, cardiovascular myopathy, and many more. In radiology, AI is used varyingly – its application is being experimented with across the healthcare dissemination pathway to identify the optimal implementation and deployment methodology.
AI solutions, while being highly agile in screening and detection of abnormalities, can also be successfully deployed to optimize radiologists’ workflows, facilitate quantitative radiology, and assist in the triage of critical cases. With the pandemic also weighing in on the current healthcare infrastructure, we see a paradigm shift gradually leading to AI-enabled healthcare replacing legacy infrastructure.
Earlier this year, the United Kingdom declared its goal of harnessing digital expertise and Artificial Intelligence (AI) to drive transformation within the NHS and social care. Similar adoption models are being discussed in hospitals and healthcare networks across the globe. At present, the workload on the radiology workforce is staggering, with a radiologist required to read a scan every 3-4 seconds. An AI-enabled workflow can alleviate this issue. Moreover, it can help bring down the intra and inter-reader variations. While the need for intelligent technology has been established, questions on how, what, and where, remain an open-ended discourse across the medical fraternity. It is in this context, Qure.ai has launched its first AI Playbook, curated especially for NHS Trusts.
Access the playbook below.
Read the full playbook
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