Decision-Making AI – Shaping the Future

Decision Making AI

Healthcare is an industry in flux. An industry that’s faced the extraordinary challenge of the COVID-19 pandemic and discovered the value in technology as a tool to help manage workloads, systems, patient care and practitioner wellbeing. Technology has been rapidly transforming most sectors over the past few years. The evolution of artificial intelligence (AI), deep learning, machine learning, automation and the Internet of Things (IoT)  has seen technology adapt to new models of operation and introduce new ways of thinking and doing business. 

In a recent report released by the World Economic Forum in September 2020, the analysts highlight the immense value that can be derived from these technologies, specifically in terms of decision making and quality of life. The report states that: ‘AI can be used to enhance the accuracy of decision making and to improve lives through new apps and services.’ It has applications in weather and climate management, in infrastructure, in business and in healthcare. It can be used to change how manufacturing, mining and industrial organizations approach resources and productivity, and it can be used by the public sector to improve quality of service for its citizenry. 

Decision-making AI has become far more than the story once told by the hype that preceded it. As its capabilities have grown, so has its applications and its potential. From fair to average chatbots deployed by organizations in call centers to highly evolved neural networks that can interpret information at speeds the human mind cannot conceive. A recent survey published on InsideBigData and undertaken by Interactions and The Harris Poll examined the adoption of AI and how it has influenced business, and the findings were interesting to say the least. 

The poll revealed that around 99% of mid-to-high level executives were using AI technology within their business and 96% believed it was critical to the business and its success. The use cases for AI are usually optimization, business process automation, cost reduction, and customer experiences. While these are the factors that influence how executives make decisions around the implementation of AI, decision making AI is now entering the workplace on the back of these criteria and changing the way organizations approach strategy, customer, patient and market. 

Decision-making AI – the application of intelligence

In Hong Kong, venture capital firm Deep Knowledge Ventures recently put an algorithm on its board of directors.  The algorithm examines vast swathes of data so that it can provide board members with insights into a variety of business factors such as finances, drug development, and funding. This decision-making AI was designed to support the organization in its commitment to use data more effectively and to leverage the insights in the data to deliver on its business strategy. The program – called Vital – offers up detailed recommendations to the board members and has already facilitated two investment decisions.

Flying out of Hong Kong and into the aviation industry, decision-making AI has been used by Airbus to help improve business decision making and in the development of aircrafts. The technology, known as Skywise, has been implemented to sift through the data usually locked into siloes and provide engineers and decision makers with highly relevant and targeted insights.

And way back in 2013, Proctor & Gamble put data visualization at the center of its management system so that data would always be at the forefront of decision making. Called the Decision Cockpit, the platform is accessible by employees of the firm and can be used to unpack product impact, growth, financials and so much more. It was forward thinking for the time, today it’s a mark of futureproof genius. As Accenture puts it in a recent study entitled ‘Data to decision-making: is your board AI ready?’, “Bringing AI and analytics to the boardroom will help leaders address these challenges and create lasting impact across the whole enterprise, proving intelligent technologies are no longer a business advantage – they could be an operational imperative.”

Today, there are multiple examples of decision-making AI in every sector; from Volvo using AI to improve vehicle safety to BP using decision-making AI to manage performance on its equipment. It’s permeating every sector with its ability to analyze, interpret and transform data at speed.

Decision-making AI – the healthcare intellect

But decision-making AI is not just for the business sector, it’s making extraordinary inroads into healthcare and changing the shape of patient care and practitioner capabilities. In the healthcare sector, decision-making AI is having a significant impact on radiology. Radiologists have to manage incredibly complex and challenging workloads that put them under immense pressure on a daily basis. These workloads were estimated, back in 2015, to expect the radiologist to interpret one image every three to four seconds. That number is not only incredibly high, but it puts the professional radiologist under pressure to deliver high-quality analysis in an unreasonable amount of time. It’s also likely that the number of mages which an average radiologist has to interpret has only increased since 2015 as new methods of scan and image development come to the fore.

Which is where decision-making AI is coming to the fore. A study undertaken by researchers and published in 2019 found that automated deep neural networks could rapidly diagnose intracranial hemorrhages in patients with acute neurological symptoms at extraordinary speeds – reducing the time to diagnosis from minutes to seconds. It showed the immense value that AI could bring to the ED department and professional when workloads are only increasing on a daily basis.

Defined as the ‘colleague that never sleeps’, decision-making AI can be used as a tool to provide additional insights to a medical profession burdened by intense workloads and the need to provide more in-depth patient care. AI can reduce reporting times, improve analysis, automate basic tasks and integrate seamlessly with radiology workflows. It’s the extra pair of eyes that every department needs right now.

Aidoc is one such solution, providing AI decision-making support tools across multiple locations and geographies, providing increased clinician support and workflow optimization to burdened systems and radiologists. Abnormalities are quickly identified and prioritized and radiologist workloads balanced more effectively. Aidoc is designed to support the radiologist that works a 10-12-hour day just to keep up with immense workloads and industry requirements.

While decision-making AI in healthcare is still finding its proverbial feet, it has already seen significant results. The future still lies in the hands of the medical professionals, but they are now being supported by technology that understands their unique needs and environments and reduces the stresses that they experience on a daily basis.

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