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Examples of AI in Healthcare: The Evolution of Intelligent Care

It’s a revolution. A transformation of healthcare as artificial intelligence (AI) solutions and deep learning platforms and intelligent algorithms become increasingly powerful and capable. AI in healthcare has moved from shifty discussions in dark corridors and the annals of science fiction into the machines and workflows of medical institutions across the globe. From radiology to preventative screening to emergency care [link to previous post], AI in healthcare has become a powerful tool designed to support healthcare practitioners and institutions as they manage increasingly weighty and challenging workloads. 

In fact, anyone looking at the leaps and bounds being made in the healthcare industry can comfortably say that this is one sector that is seeing unparalleled growth and innovation right now. But, amidst the hype, what are the real-world examples of AI in healthcare? 

Real World Examples of AI in Healthcare

Dreamed Diabetes uses both AI and cloud technology to create a solution that can aggregate and transform patient data into actionable insights that can be used to save lives. The solution also offers the practitioner relevant information that allows assessment of  patients in real-time and enables improvement of patient self-management and care. 

Another company, BenevolentAI has been recognized as a leader in the AI market in the UK with it’s goal of finding medications for diseases that have no treatment.  The company is focused on using AI to discover new drugs at speed, and has been one of the companies on the COVID-19 pandemic front line. Currently, BenevolentAI is making headlines for its discovery of Barictinib which is an already approved drug for rheumatoid arthritis, as a potential treatment for COVID-19. While there remains testing that needs to be done, this would be a significant boost to the organization’s reputation if the drug turns out to be what they hope. 

Another real-world example of AI in healthcare is the use of AI in oncology. A recent paper published on the CancerNetwork showcased how convolutional neural networks (CNNs) are being used in digital imaging to detect various types of cancer.  The same paper pointed out how AI is beginning to shine in the field of radiogenomics where radiographic image analysis is ‘used to predict underlying genotypic traits’. Aidoc, a rising star in the AI in radiology realm, has been developing AI solutions for the radiology sector for several years now and achieved several notable accreditations from organizations such as the FDA for its algorithms. A department of Radiology and Biomedical Imaging at a leading teaching hospital in the northeast United States has improved report turnaround time and reduced the length of patient stay with the implementation of Aidoc AI. The hospital is known for being at the forefront of innovation, introducing the latest in artificial intelligence (AI) technologies, driving research and education, and maintaining the highest levels of patient care and satisfaction. Aidoc’s solutions are currently deployed at over 350 leading medical centers worldwide. The company has five FDA cleared solutions and six CE marked solutions.

AI in Healthcare: Pushing the needle forward

The nature of AI in healthcare is that it can have applications across multiple verticals and within variable situations. The recent pandemic has certainly pushed the needle forward when it comes to many of these innovations, particularly in the realms of imaging and telehealth/telemedicine. Due to the limitations imposed by lockdowns and social distancing, many people have struggled to find adequate patient care or with understanding when they are supposed to seek out medical attention. 

This issue has been addressed through the use of AI in healthcare companies and the implementation of intelligent chatbots. Designed to minimize patient stress and reduce their being inundated with complex terminology and acronyms, solutions are being developed by companies such as Google and Nuance to create accessible pathways for patients and practitioners. These are not just limited to chatbots that can assess patients, thereby minimizing impact on the healthcare industry when it comes to the volume of diagnoses, but also in triaging patients [link to our feature], managing admin, and improving revenue streams. The NHS has been using AI systems to rank patients according to scales of importance so that they can triage them more effectively. This has become a massively important development for the burdened system that’s expecting a treatment queue of 10 million patients by the end of 2020. 

Finally, in a report released by PwC earlier in 2020, the research company identified six areas that were most being transformed by AI in healthcare. These included: keeping track of supplies, turning health records into real-time responses, forecasting hospital demand, reducing re-admissions, and monitoring patients with virtual nurses. For PwC, these shifts in AI potential and ability mark the start of an incredibly exciting time for the healthcare industry – each one already supported by a case study that emphasizes the value of an AI in healthcare solution implemented intelligently. 

What lies ahead? This remains to be seen in light of how the needle is being directed by the pandemic and the evolving demands of the healthcare industry. There is an urgent need to reduce the pressure on practitioners, particularly in the radiology sector, and to help them manage their bursting workloads with greater efficiency and reduced risk. What lies ahead is a time for AI in healthcare to shine and for AI healthcare companies to dig deep into innovation to deliver the remarkable and the extraordinary.

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