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Artificial Intelligence in Healthcare

Ready for its closeup, or not ready for primetime?

Headlines about the advent of artificial intelligence, AI, in pretty much every sector of human life or enterprise seem to be a daily occurrence. Other phrases that get thrown around in stories about AI are machine learning, deep learning, neural networks, and natural language processing.

Here’s a handy list, from the transcription company Sonix, which uses some of these AI tools to drive their service:

  • Artificial Intelligence (AI) –the broad discipline of creating intelligent machines
  • Machine Learning (ML) –refers to systems that can learn from experience
  • Deep Learning (DL) –refers to systems that learn from experience on large data sets
  • Artificial Neural Networks (ANN) –refers to models of human neural networks that are designed to help computers learn
  • Natural Language Processing (NLP) –refers to systems that can understand language
  • Automated Speech Recognition (ASR) –refers to the use of computer hardware and software-based techniques to identify and process human voice

A lot of the stories I see about AI are focused on how it might impact, improve, or otherwise influence healthcare. Depending on who you listen to, it sounds like AI is already diagnosing cancer successfully – here are two pieces, from science savvy sources, on how that’s working, “AI is already changing how cancer is diagnosed” from The Next Web, and “AI matches humans at diagnosing brain cancer from tumour biopsy images” from New Scientist, for your reading pleasure.

As aspirational as the idea of AI in healthcare is, and despite the fact that it’s showing some promise in cancer diagnosis, I’m not thinking that it’s time for the champagne, balloons, and glitter … yet.

One of the biggest barriers to AI is the same barrier everyone – on both sides of the stethoscope, and all the way up to the c-suite – in healthcare confronts daily: data access and liquidity. Data fragmentation is rife across the entire healthcare landscape, with EHR systems that don’t talk to each other well (if at all), and insurers unwilling to open their datasets to anyone under cover of “trade secrets.” In “The ‘inconvenient truth’ about AI in healthcare” in the journal Nature, the authors (British, so this is not just an American problem) point out that, “Simply adding AI applications to a fragmented system will not create sustainable change.” Healthcare systems may be drowning in data (they are), but tools to parse all those data lakes into actionable insights aren’t able to bust the dams holding in that data.

Access is one barrier. Another is the ethics of using AI in healthcare. The American Medical Association’s Journal of Ethics devoted an entire edition to that issue in February 2019, with AMA J Ethics editor Michael J. Rigby calling for deeper discussions about preserving patient preferences, privacy, and safety before implementing AI technology widely in healthcare settings. He particularly notes the impact AI could have in medical education, with medical education being shifted from a focus on absorbing and recalling medical knowledge to a focus on training students to interact with and manage AI-driven machines; this shifting would also require attention to the ethical and clinical complexities that arise when humans interact with machines in medical settings.

AI, across all uses, but particularly in healthcare, has to take a long, hard look at how bias can spread algorithmically, once it’s baked into the code that’s running the machines. There are data scientists doing bias detective work, but will the detectives be able to prevent bias, or just bust perpetrators once the biased outcomes appear?  Stay tuned on that one.

Is there an upside to AI in healthcare? Absolutely, *if* the ethical issues on privacy and error prevention, and the practical issues on data access, are addressed. AI could pave the way to fully democratizing information, both for patients and front-line clinicians. It could liberate all clinicians from data-input drudgery, or “death by a thousand clicks.” The Brookings Institution has a solid report, “Risks and remedies for artificial intelligence in health care,” as part of its AI Governance series, that breaks down the pros and cons.

Circling back to the question in the headline, is AI in healthcare ready for primetime? This person’s answer: it depends. I think that rigorous study, in the development of AI in medicine and its use in the healthcare system, is required as an ongoing feature of AI tech used in human health. Upside there? A whole new job classification: AI oversight and management.

How Healthcare May Be Improved With Artificial Intelligence

If you have not been up to date with healthcare news, or do not work in any healthcare related field, you may be unaware of the gradual increase how reliant the sector is on technology. Every facet of society has been on an upward climb with how digitized it is, and healthcare is no exception. From breakthroughs as interesting as robotic surgery to standardizing electronic patient notes, both primary and secondary care have grown accustomed to the benefits of how artificial intelligence can benefit them.

In healthcare, introducing new treatment whether based in technology or pharmaceuticals is highly expensive, though great efforts are being taken to increase efficiency, reduce human errors and improve healthcare overall. In the long running of things, this would save the healthcare economy billions in coming decades.

Genomics

There has been a public declaration made by IBM Watson Health to incorporate artificial intelligence to the ongoing battle against cancer. The focus currently lies with later stage cancer patients who are at their most critical points. This is because it is likely current treatments have failed for them, or aren’t strong enough. New treatments could offer them the best chances when facing their life or death situations.

Specific genetic factors involved in cancer can be identified and targeted with idealized therapies. This offers hope to many Veterans in the US, and cancer patients worldwide.

Drug Discovery

It has been about three whole decades since a new effective antibiotic has been discovered. This has led to a seemingly losing battle with the emergence of more superbugs (antibiotic resistant pathogens) significantly often. The journey to discovering new drugs is very expensive, meaning many drug companies have slowed down the process of discovery. However, Pfizer’s use of IBM Watson (technology that utilizes machine-based learning) is pioneering the path to finding new drugs that are active for cancer and immune therapies.

Other drug companies such as Sanofi are using artificial intelligence to find new therapies for metabolic disease; Genentech are also leading the way in cancer research with artificial intelligence from Cambridge, Massachusetts.

Robotic Surgery

The correct term for this is robot-assisted surgery, because though it looks like a robot is handling the surgery from the operating theatre, there is actually a surgeon (or multiple surgeons) that are controlling the robotic tools remotely. This has been rolled out successfully in multiple countries so far. These include the United Kingdom and Dubai. The major benefits of robot-assisted surgery is increased precision and accuracy. There is less room for human error, and more room for improved patient care.

Secondary Prevention

One of three or sometimes four main branches of prevention, secondary prevention relates mostly to medical imaging. There has been a huge surge of technological advances in this area in the past century. The simple ultrasound has become 3D imaging and the simple radiograph has become detailed computerised tomography. New approaches can now be taken, that reveals more information about patients. This leads to clearer imaging, faster diagnosing and better results.

Personalized Medicine

Genetic screening has been more incorporated into healthcare since the sequencing of the human genome in recent decades. With genetic information and associations readily available, more accessible means of accessing patient DNA have been developed. There are now easy methods of reaching a patient’s genetic code and assessing their risk for certain health issues that carry genetic risks.

“Polygenic scoring weighs the linear combination of multiple small genetic variations and are used in predisposition assessment,” says Mary Crawford, tech blogger at Australia2Write and Write Myx.

Visual Assisting

Nursing is investing in the development of virtual assistants, which can take over the role of healthcare assistants and push the healthcare staff population to higher fields of work. Healthcare providers will then be able to maintain continuous contact with patients.

Better Data Security

A major leap in healthcare is digitizing patient records, and rolling out a singular way of standardizing them across the country. Though this is extremely useful for transferring patients from healthcare provider to healthcare provider, it creates room for a cyber-attacks that will steal sensitive data.

“As artificial intelligence increases with patient data storage, it also increases with cybersecurity. Extra security is essential to patient protection,” says Erick Schmid, data analyst for Brit Student and Next Course Work.

Discussing how healthcare may become revolutionized by artificial intelligence may conjure up images of the 1985 movie Daryl. However, the movements are very much real and non-fictional. Productivity is on the rise and medicine has become more business-minded.

Due to its benefits, artificial intelligence is certainly gaining popularity in the healthcare industry and there are developments every year. There are predictions that the involvement of artificial intelligence will grow by 1000% by 2015, pushing it to become a 13 billion dollar industry.

Michael Dehoyos is a medical Blogger at Phd Kingdom and Academic brits. He assists companies in their marketing strategy concepts, and contributes to numerous sites and publications. Also, he is a writer at Case Study Help, academic service.