Can Machine Learning be of use in the healthcare industry where human care and expertise is so critical? Turns out it can be, and that too in a life-changing way.
Machine Learning, which is a subset of the bigger cognitive technology called Artificial Intelligence is shedding a positive influence on every imaginable industry. Healthcare, which for long has remained the forte of human expertise is also setting the stage for integrating cognitive systems into clinical workflows.
Does it sound like a slice of fantasy or fiction? Well, it is just the future that we are inching closer to, or perhaps one that has already arrived. Machine Learning can help unearth insights from heaps of healthcare data originating from patient wearables, online lab reports, offline doctor prescriptions and much more.
The beauty is that 90% of the digital data we have today was created only in the past 2 years IBM Industry Insights. Imagine combining offline data like medical records, doctor prescriptions, patient hand notes, etc. to an ML system that can draw inferences and predict patterns? It will change forever how doctors diagnose symptoms and prescribe remedies.
In other words, AI and Machine Learning will shape the future of healthcare. Eric Lefkofsky, co-founder and CEO of Tempus – an ML-based clinical data analytics company says,
“ML would empower doctors to see through a patient’s historical and real-time data to arrive at proactive prescriptions. It would help prepare a patient for preventing disastrous diseases like cancer.”
The PwC 2017 Global Digital IQ Survey reports that 31% of healthcare execs rate AI as the most disruptive tech in the industry.
The study further found that at least 63% of the healthcare execs are expected to invest substantially in AI in the next three years.
Will AI replace doctors? Absolutely, not. AI will help doctors take away the grudge work they have to do to arrive at crystallized information on the basis of which patient care can be delivered.
So, how exactly would Machine Learning make doctor and patient lives better? Here are some ways we can expect this to happen.
Data-driven Cancer Prediction
Machine Learning is putting data in the hands of doctors to detect probably-fatal disease conditions like cancer. Google’s DeepMind Health is a fine example of how machine learning can be harnessed to detect health issues from patient data. IBM also released its own healthcare-centric ML platform called IBM Watson Health for Genomics which would enable oncologists to use cognitive computing for genomic tumor sequencing.
Such data would enable oncologists to derive accurate predictions of a patient being affected by cancer in the future. The data-driven model for cancer prediction would help oncologists chart a personalized cancer treatment journey depending on the cancer growth stage at which the patient. It would also remove the guesswork that often doctors resorted to determine the pace at which cancer was growing.
The good news is that ML data models can be trained using Natural Language Processing to understand medical literature so that they can infer symptoms and possible diseases just like a human doctor would do. The doctor can cross-check the findings and approve or dispel the ML prediction.
Personalized health coach
This is the era of the Quantified Self. Gary Wolf and Kevin Kelly coined the term and started the movement in 2007 since when it has become sort of modern-day lifestyle. As gadget loving beings, we collect information about ourselves and our health from several devices. The data so collected from wearables to smartphone dashboards and also clinical equipment help paint a big picture of an individual’s overall physical health condition.
Imagine the power to crunch such data and extract accurate information? It would help clinicians to craft personalized treatment plans that is best suited to the health and fitness goals of the individual.
Non-invasive Medical imaging
Image recognition using Machine Learning throws open infinite possibilities in healthcare. ML systems can be trained to look at images of skin, eyes, fractures, etc. to detect the exact level of damage. The system can also spot anomalies by comparing the image patterns to ideal health patterns as prescribed by medical literature.
A fine example of non-invasive medical imaging is the Dermatoscope engineered by the Computer scientists at Stanford. The handheld microscope can be pointed at the skin area of the patient to spot anomalies that indicate the possibility of a skin cancer. And, the diagnosis done by the machine algorithm has been proven to match the expertise of board-certified dermatologists.
Final Words: ML Would Foster better healthcare
We can safely muster than machine learning would foster better healthcare facilities. Doctors would become faster at detecting symptoms and taking proactive measures to prevent the disease condition. Patient data would become comprehensive as all data can be assembled under a single repository for intelligent diagnosis. By all means, we are entering the days of possibilities to clinical realities using machine learning.