6 ways AI will impact Healthcare
Artificial intelligence is set to become a transformational force in healthcare by offering many advantages over traditional analytics and clinical decision-making techniques. Learning algorithms can become more precise and accurate as they interact with training data, allowing humans to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes.
At the 2018 World Medical Innovation Forum (WMIF), leading clinical members showcased several technologies and areas of the healthcare industry that are most likely to see a major impact from artificial intelligence within the next decade.
1. Making smartphone ‘selfies’ into powerful diagnostic tools
The quality of mobile phone cameras is improving every year, and many are already advanced enough to produce images that are viable for analysis by AI algorithms. Researchers in the UK have even developed a tool that identifies developmental diseases by analysing images of a child’s face. The algorithm can detect discrete features such a child’s jawline, eye and nose placement, and other attributes that may indicate abnormalities. Currently, the tool can match images to more than 90 disorders to provide clinical decision support.
2. 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 tumours. 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. Progress is already being made with the exciting development of checkpoint inhibitors which block some of the proteins made by some of the immune cells.
3. Creating more precise analytics for pathology images
Pathologists provide one of the most significant sources of diagnostic data for providers across the spectrum of care delivery. To put this into context, 70% of all decisions in healthcare are based on pathology results so the more accurate results obtained, the sooner pathologists are able to make the right diagnosis. AI comes into play by providing analytics that can drill down to the pixel level on extremely large digital images enabling providers to identify nuances that may escape the human eye.
Artificial intelligence can also improve productivity by identifying features of interest in slides before a human clinician reviews the data. AI can screen through slides and direct clinicians to the right thing to look at so they can assess what’s important and what’s not. That increases the efficiency of the use of the pathologist and increases the value of the time they spend for each case.
4. Revolutionising clinical decision making with artificial intelligence at the bedside
Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers in to problems long before they might otherwise recognise 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. Typically, providers must visually inspect EEG data from these patients, but this process is time-consuming and subjective, and the results may vary with the skill and experience of the individual clinician. However, 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.
5. Monitoring health through wearables 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 analysing 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.
6. Developing the next generation of radiology tools
Radiological images obtained by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the inner workings of the human body. But many diagnostic processes still rely on physical tissue samples obtained through biopsies, which carry risks including the potential for infection. Artificial intelligence will enable the next generation of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases, experts predict. Succeeding in this may enable clinicians to develop a more accurate understanding of how tumours behave as a whole instead of basing treatment decisions on the properties of a small segment of the malignancy.
With the examples cited above, it clear to see that by powering a new generation of tools and systems that make clinicians more aware of problems, 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.
But AI isn’t the only technology poised to revolutionise healthcare. Take a look at how the internet of things is transforming patient care by reading our recent blog here.