The inefficiencies of everyday life in most matters may not be as consequential as life and death. But the same luxury cannot be afforded to healthcare. The importance of efficiency in healthcare makes it a unique area where the scope of computerization is appreciable. Moreover, the availability of large volumes of data means there is a big data opportunity rather than a big data deficiency. This is largely thanks to the medical community adopting considerable measures to make electronic health record systems easily available to physicians and patients.
In order to put all this information to proper use, the MedTech industry has started leveraging AI and machine learning.
“On top of all this data, you have applications or machine learning algorithms, things that you’re doing against the data to allow you to gain insight, to make decisions and to optimise the outcomes,” said Eric Schnatterly, Vice President IBM Systems for Cloud Platforms.
Now, AI and machine learning are transforming the healthcare industry, potentially changing outcomes for good, and revolutionizing the way doctors provide medical care. AI in Healthcare is improving diagnostics/decision making, predicting outcomes, and paving way for a new generation of personalized HealthTech.
During the inception of Healthcare NExT, a venture by Microsoft, Corporate Vice President of the program, Peter Lee said “(Tackling healthcare problems) is a bigger challenge. But we believe technology – specifically the cloud, AI and collaboration and business optimization tools – will be central to healthcare transformation.”
With Industry analysts IDC expecting more than 30 percent of providers to start using cognitive analytics on patient data by 2018, the future of AI in MedTech, and its consequent implications on healthcare look exciting.
One of the major contributions of AI in recent times is its role in tracking and assisting people on their health. Wearables and mobile applications have encouraged health management in individuals and are already instilling a sense of healthy living. They now play a major role in supporting the proactive management of a healthier lifestyle.
Though it is early to comment on the adoption of AI chatbots, current trends of requirement fulfilment are rather encouraging. “Chat-bots will continue to get more intelligent over time, thanks to AI and machine learning techniques that will make them very efficient, and of course, more timely than a human can ever be,” says Khal Rai, an AI expert at SRS Health.
New platforms are using a combination of genetic data, data from wearables (blood test data, heart data) and data from a user’s health history, to continuously monitor and offer suggestions for maintaining health.
“The healthcare sector is regularly targeted by cyber-attacks and the goal of the NHS to go paperless by 2020 and to digitise all data makes for even richer pickings”, says Matt Walmsley, EMEA director at cybersecurity company Vectra Networks.
The key to cybersecurity is visibility; “you cannot protect what you cannot see,” says Derek Manky in his article on cybersecurity for Fortinet. The challenge, therefore, is to take advantage of AI to deal with two critical issues: widen visibility and improve collaboration. Big Data algorithms can work on intelligence, in sync with AI and machine learning, to neutralize the threat by detecting/predicting them and their behaviour.
“We’re seeing the need to increasingly automate security in healthcare, as legacy systems and manual human processes are unable to respond in a timely manner. AI can track and locate the behaviour of an attacker inside a network before a data breach occurs or ransomware has the chance to take an organisation hostage,” Walmsley added.
Before data analytics, diagnostic programs concerning specifics of diseases were created using predefined assumptions. With Big Data, the ability to store and retrieve loads of medical information (symptoms, causes and treatments) containing journals is much faster than humanly conceivable.
“In today’s world, data will enable AI machines to learn and understand new medical functions, and then critically provide humans e.g. doctors with the necessary information to diagnose problems,” says Dr. Joseph Reger, CTO, Fujitsu EMEIA.
AI in healthcare is increasingly being used to predict the occurrence of cancers and also improving imaging modalities to better read doubtful nodules and lesions in cancer patients when using CT scans, MRI data etc.
The healthcare sector has always been a tad shorthanded when it came to customer service and AI is here to even the odds.
“While the reasons for customer service challenges are varied and widely debated, there is no doubt technology can help to deliver positive patient outcomes while driving efficiencies. Empowering patients to self-help rather than contact NHS will help reduce the number of patients who need to see a GP,” says Nick Wilson, managing director, public sector, health and care, at software company Advanced.
One of the challenges to customer-driven healthcare, the inability to provide a more personalized service, is being overcome by companies like Amazon, Google and Target using machine learning.
“Amazon does not consider everyone to be the same consumer, it understands you based on the data. That’s the foundation for transformation. The opportunity now is to blend this customization and understanding into healthcare,” points out Leonard D’Avolio, CEO and co-founder of machine learning startup Cyft.
For example, Olivia, a virtual nurse app based on AI, gathers data by asking questions that a physician normally would. It then processes it in its unique algorithm to suggest the best course of action.
Machine learning based systems are expanding at a rapid pace in healthcare, enabling organizations of all sizes to transform their business models. Hence, the age where we look to bots to make new appointments and even physicians using AI-based systems on a daily basis is not far off. We wait with bathed breaths!