Saturday, 13 August 2016

House MD vs Doctor #AI- Who will turn out to be the better by @RoshiniBR


House MD vs Doctor AI- Who will turn out to be the better diagnostician?

Do a google search for “all-inclusive beach holiday” and all you see for the next weeks are advertisements for all-inclusive holidays following the virtual you. Google knows you better than your family or friends- creepy but true! Whether or not we realize it, we have made the decision to donate our data to the virtual world.

Can we put our data to better use – to improve healthcare on a scale unimaginable a decade or two ago?

Artificially intelligent systems in healthcare


AI systems feed on big data. Big data is nothing but a massive amount of data, the sheer size of which makes data analysis a challenge. AI systems are being developed to analyze and recognize meaningful patterns out of this complex data. Of particular significance is deep learning, a branch of AI which attempts to mimic the thinking part of the human brain. Several startups are now attempting to put AI, particularly deep learning to meaningful use in healthcare.

Diagnosing diseases: Clinical diagnosis is essentially a data problem, says founder of Enlitic, a machine learning startup focusing on data-driven medicine. They aim to revolutionize clinical diagnosis by helping physicians to automatically screen for specific diseases using their proprietary technology. Another startup, Deep Genomics, is approaching diagnosis from a genomic perspective. They capitalize on machine learning technology to link genetic variations to diseases. As a cherry on top, a study published as recent as last week reported that AI-enabled automated cancer detection perform as well as approaches that require costly clinician input.


"We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not"- author of the study, Shaun Grannis M.D., M.S.

Educating Patients: IBM is teaming up with the American Cancer Society to create an adviser for cancer patients, powered by IBM’s Watson health - health division of its AI brain. It would be designed to provide cancer patients or their caregivers with personalized guidance based on the patient's particular disease stage and treatment. Watson would sift through countless digital sources like health websites and draw relevant and trustworthy information catering to individual needs. 

Improving clinical trials: Many clinical trials fail because patients fail to take their medications. AiCure is addressing this issue using AI to monitor medication adherence thereby increasing trial success.  They directly monitor patients using artificial intelligence on mobile devices via an app which collects real-time dosing data on a centralized and cloud-based platform. IBM Watson is trying to solve another issue facing clinical trials - enrolling patients. Watson would sift through clinical trial data at Mayo Clinic and in public databases, such as ClinicalTrials.gov. and match patients more accurately and consistently to clinical trial options.


Using Watson's cognitive computing capabilities, Mayo Clinic can consistently offer more cutting-edge medical options to patients and conclude trials faster”  - Mike Rhodin, senior vice president, IBM Watson Group.

Accelerating drug discovery: Drug discovery is a lengthy, complex, and costly process, rooted with a high degree of uncertainty that a drug will actually succeed. Several machine learning startups are trying to solve this long-standing predicament in pharmaceutical drug development. Berg Health combines systems biology with its proprietary artificial intelligence machine learning analytics program to bring down the time for cancer drug development to almost half. Atomwise's drug discovery AI platform "learns like a human chemist" using deep learning algorithms and supercomputers.


“Simulating billions of virtual medicines to find potential commercial candidates in weeks? That’s what we call truly transformative.”- Matt Ocko, Managing Partner of Data Collective, a venture firm backing Atomwise

Reducing hospital costs: Readmissions are costly for hospitals as payers are reluctant to reimburse preventable readmissions. Hindsait, an artificial intelligence technology provider, helps identify patients who are nearing the end of their stay and suggests whether keeping them might in fact be more cost effective than risking readmission. It does so by applying AI based data analysis to large health datasets.

These and many more upcoming developments in cognitive computing in healthcare leaves us with the thought - will AI systems perform better diagnosis than out best diagnosticians. Google's AI program, AlphaGo beating Go world champion and IBM's Watson beating humans in Jeopardy seems to suggest so.

Note: Interestingly, there were many recent publications that influenced the direction of this article. Application of AI technologies in healthcare is growing with new collaborations being made at this very moment. Interesting times indeed for all those who influence and follow these developments. 
Author
Roshini Beenukumar
Dr. Beenukumar is a molecular biologist turned science writer. During her PhD, she studied how cancer cells behave the way they do by exploring the humble yeast. Currently, she works as a freelance science/technical writer in the Life Sciences industry . She enjoys communicating science to the public and discussing new ideas in the interface of medicine and technology. She spends her spare time getting lost in a book or in nature.

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