From using machine learning algorithms in creating better treatments for depression to diagnosing cancer faster, the future of artificial intelligence in healthcare has never been brighter. Scientists are using fast-evolving techniques and machines from research in AI to better serve their patients. The change that AI is bringing into the health sector has been received with open arms. This is so largely because machine learning algorithms present a more precise and accurate result from testings with data than traditional analytics and clinical decision-making techniques. This brings unprecedented progress poised to foster exponential advancement in healthcare. In an article posted on Health Analytics– a health-focused website based in Massachusetts, USA, Jennifer Bresnick a health care technology and data expert, looked at ways in which artificial intelligence will impact the industry even further.
See below some of the points she touched on below:
Advancing the Use of Immunotherapy for Cancer Treatment
Immunotherapy is one of the most promising avenues for treating cancer. By using the body’s own immune system to attack malignancies, patients may be able to beat stubborn tumors. However, only a small number of patients respond to current immunotherapy options, and oncologists still do not have a precise and reliable method for identifying which patients will benefit from this option.
Machine learning algorithms and their ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.
“Recently, the most exciting development has been checkpoint inhibitors, which block some of the proteins made by some times of immune cells,” explained Long Le, MD, Ph.D., Director of Computational Pathology and Technology Development at the MGH Center for Integrated Diagnostics. “But we still don’t understand all of the disease biologies. This is a very complex problem.”
“We definitely need more patient data. The therapies are relatively new, so not a lot of patients have actually been put on these drugs. So whether we need to integrate data within one institution or across multiple institutions is going to be a key factor in terms of augmenting the patient population to drive the modeling process.”
Turning the Electronic Health Record into a Reliable Risk Predictor
EHRs are a goldmine of patient data, but extracting and analyzing that wealth of information in an accurate, timely, and reliable manner has been a continual challenge for providers and developers.
Data quality and integrity issues, plus a mishmash of data formats, structured and unstructured inputs, and incomplete records have made it very difficult to understand exactly how to engage in meaningful risk stratification, predictive analytics, and clinical decision support.
“Part of the hard work is integrating the data into one place,” observed Ziad Obermeyer, MD, Assistant Professor of Emergency Medicine at BWH and Assistant Professor at HMS. “But another problem is understanding what it is you’re getting when you’re predicting disease in an EHR.”
“You might hear that an algorithm can predict depression or stroke, but when you scratch the surface, you find what they’re actually predicting is a billing code for stroke. That’s very different from stroke itself.”
Relying on MRI results might appear to offer a more concrete dataset, he continued.
“But now you have to think about who can afford the MRI, and who can’t? So what you end up predicting isn’t what you thought you were predicting. You might be predicting billing for a stroke in people who can pay for a diagnostic rather than some sort of cerebral ischemia.”
EHR analytics have produced many successful risk scoring and stratification tools, especially when researchers employ deep learning techniques to identify novel connections between seemingly unrelated datasets.
But ensuring that those algorithms do not confirm hidden biases in the data is crucial for deploying tools that will truly improve clinical care, Obermeyer maintained.
“The biggest challenge will be making sure exactly what we’re predicting even before we start opening up the black box and looking at how we’re predicting it,” he said.
Monitoring Health through Wearable and Personal Devices
Almost all consumers now have access to devices with sensors that can collect valuable data about their health. From smartphones with step trackers to wearables that can track a heartbeat around the clock, a growing proportion of health-related data is generated on the go.
Collecting and analyzing this data – and supplementing it with patient-provided information through apps and other home monitoring devices – can offer a unique perspective into individual and population health.
Artificial intelligence will play a significant role in extracting actionable insights from this large and varied treasure trove of data.
But helping patients get comfortable with sharing data from this intimate, continual monitoring may require a little extra work, says Omar Arnaout, MD, Co-director of the Computation Neuroscience Outcomes Center and an attending neurosurgeon at BWH.
“As a society, we’ve been pretty liberal with our digital data,” he said. But as things come into our collective consciousness like Cambridge Analytica and Facebook, people will become more and more prudent about who they share what kinds of data with.”
However, patients tend to trust their physicians more than they might trust a big company like Facebook, he added, which may help to ease any discomfort with contributing data to large-scale research initiatives.
“There’s a very good chance [wearable data will have a major impact] because our care is very episodic and the data we collect is very coarse,” said Arnaout. “By collecting granular data in a continuous fashion, there’s a greater likelihood that the data will help us take better care of patients.”
Making Smartphone Selfies into Powerful Diagnostic Tools
Continuing the theme of harnessing the power of portable devices, experts believe that images taken from smartphones and other consumer-grade sources will be an important supplement to clinical quality imaging – especially in underserved populations or developing nations.
The quality of cell phone cameras is increasing every year and can produce images that are viable for analysis by artificial intelligence algorithms. Dermatology and ophthalmology are early beneficiaries of this trend.
Researchers in the United Kingdom have even developed a tool that identifies developmental diseases by analyzing images of a child’s face. The algorithm can detect discrete features, such as a child’s jawline, eye and nose placement, and other attributes that might indicate a craniofacial abnormality. Currently, the tool can match ordinary images to more than 90 disorders to provide clinical decision support.
“The majority of the population is equipped with pocket-sized, powerful devices that have a lot of different sensors built-in,” said Hadi Shafiee, Ph.D., Director of the Laboratory of Micro/Nanomedicine and Digital Health at BWH.
“This is a great opportunity for us. Almost every major player in the industry has started to build AI software and hardware into their devices. That’s not a coincidence. Every day in our digital world, we generate more than 2.5 million terabytes of data. In cell phones, the manufacturers believe they can use that data with AI to provide much more personalized and faster and smarter services.”
Using smartphones to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects may be able to help underserved areas cope with a shortage of specialists while reducing the time-to-diagnosis for certain complaints.
“There is something big happening,” said Shafiee. “We can leverage that opportunity to address some of the important problems with have in disease management at the point of care.”
Revolutionising Clinical Decision Making with Artificial Intelligence at the Bedside
As the healthcare industry shifts away from fee-for-service, so too is it moving further and further from reactive care. Getting ahead of chronic diseases, costly acute events, and sudden deterioration is the goal of every provider – and reimbursement structures are finally allowing them to develop the processes that will enable proactive predictive interventions.
Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers into problems long before they might otherwise recognize the need to act.
AI can provide earlier warnings for conditions like seizures or sepsis, which often require intensive analysis of highly complex datasets.
Machine learning can also help support decisions around whether or not to continue care for critically ill patients, such as those who have entered a coma after cardiac arrest, says Brandon Westover, MD, Ph.D., Director of the MGH Clinical Data Animation Center.
Typically, providers must visually inspect EEG data from these patients, he explained. The process is time-consuming and subjective, and the results may vary with the skill and experience of the individual clinician.
“In these patients, trends might be slowly evolving,” he said. “Sometimes when we’re looking to see if someone is recovering, we take the data from ten seconds of monitoring at a time. But trying to see if it changed from ten seconds of data taken 24 hours ago is like trying to look if your hair is growing longer.”
“But if you have an AI algorithm and lots and lots of data from many patients, it’s easier to match up what you’re seeing to long term patterns and maybe detect subtle improvements that would impact your decisions around care.”
Leveraging AI for clinical decision support, risk scoring, and early alerting is one of the most promising areas of development for this revolutionary approach to data analysis.
By powering a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivering care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.
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