HealthcarePatient EngagementPreventative Healthcare

How AI and Machine Learning Are Improving Healthcare Outcomes

The miracles of technology and medicine never cease to amaze.

Take a seemingly small problem with a big solution, like hearing loss and cell phones. A 60-year old woman with hearing aids and a smart phone tells everyone she knows to text rather than call her. Before answering a call, she’d have to adjust or remove her hearing aids. But then her audiologist prescribed something new.

Now with hearing aid controller apps, she can hear a caller through her aids, making her life a whole lot more convenient.

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Cognitive Analytics in Diagnostics

That’s the beauty of complex technology, such as 0pen AI ecosystems, which coordinate machine language (recognition of natural language), algorithms that track social awareness, and huge databanks to create new applications for consumer assistance. In health, it means integrating patient medical records, treatment data, and wearable health monitors to help patients proactively improve their health and health providers formulate health care plans based on patient habits.

So, when you sit down with your doctor and describe your symptoms, cognitive analytics (machine learning, data mining, and pattern recognition) takes those symptoms input to access the latest research and compares it with your own medical records history to come up with a diagnosis and treatment. Then, if you need an xray, a computer may detect any further problems.

Computers and “deep learning algorithms”.are so advanced at pattern recognition that diagnosing symptoms has become their natural specialty. After all, diagnostics is just thatrecognizing patterns that correspond to recognized diseases.

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Surgical Strikes in Pharmaceutical Development

AI drives pharmaceutical development today. Supercomputers that develop drug therapies from compiled data shorten the clinical trial testing process from years to months. Programs, like Atomwise, analyze databases of molecular structures to find drug treatments for viruses like Ebola and other diseases. The future of disease prevention lies in such AI advances.

Similarly, AI-generated machine technology recognizes patterns in genetic DNA databases. Searching through documented mutations and disease patterns for therapeutic purposes, AI correlates disease to drugs for a more precise and timely targeted treatment.

Virtual Assistants and Treatment

Need help staying on your diabetes treatment plan, taking your medications as prescribed, making follow up doctor visits, or prescription refills? Monitoring software with a human face helps you just like a personal assistant.

Or how about online consultations to a machine? Dial in or text your symptoms to Babylon’s assistant to get quick advice for common ailments. Based on patient history and common medical knowledge measured against a disease database, the virtual assistant recommends an office visit or another appropriate course of action.

Of course, there’s IBM’s Watson, the oncologist (and Jeopardy! winner). that outpaces humans in the field for devising cancer treatment options, also on machine learning, analytics, and a vast supply of clinical records, medical knowledge, and research.

And IBM’s Care Trio team, comprised of CareEdit, CareGuide, and CareView, was developed to arrive at cancer treatments and recommendations in hopes of improving survival rates and lowering treatment costs.

To round out the circle of cancer diagnosis to treatment, Pathway Genomics (affiliated with IBM) hopes to release to market an early cancer detection blood test.

Medical Records Management and Data Mining

In medical records and data mining, IBM’s cognitive health assistant “reads” radiology images to identify problems better than humans canfaster and more accurately. Medical Sieve has a gigantic store of clinical data, which is coupled with reasoning and analytical capabilities to enable it to perform routine disease detection duties.

But general records management is also a job for AI in healthcare. Google Deepmind, for example, mines medical records for more efficient and effective health care. And machines just collect, store, cross-check, and correlate records quicker and longer than human counterparts, without the need for coffee breaks.

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After Care

One of healthcare’s top goals is reduced hospital readmittance. But it’s tough getting patients to follow treatment plans upon leaving the hospital. That’s also where technology comes in.

Equipped with facial recognition and mobile technologies, AiCure takes readings of a patient’s medicine regimen to determine whether they’re taking the right medication at the right time.

Also, NextIT’s health coach, like other virtual assistants, monitors, reminds, and communicates with the patient about medication and symptoms, all of which are relayed to the patient’s physician.

And once again, IBM is at the forefront of healthy bodies with The Caféwell Concierge app, which uses “IBM’s Watson’s natural language processing (NLP)” to devise a health and wellness plan (and coaching to reach those goals) based on patient goals.

AI and Machine Learning Deliver Hope

For convenient health and wellness likely to engage patients in improving their lives, AI and machine learning deliver on an ever-expanding promise for excellent healthcare now and in the future. For those with hard-to-treat or incurable diseases, especially, these technological super-powers offer a dose of virtual hope.

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