AI News, Can A.I. ever replace human doctors? Health tech experts weigh in artificial intelligence

A machine-versus-doctors fixation masks important questions about artificial intelligence

Wallet-sized cards containing a person’s genetic code don’t exist.  Yet they were envisioned in a 1996 Los Angeles Times article, which predicted that by 2020 the makeup of a person’s genome would drive their medical care.  That idea that today we’d be basking in the fruits of ultra-personalized medicine was put forth by scientists who were promoting the Human Genome Project —

He pointed to “incentives for both biologists and journalists to tell simple stories, including the idea of relatively simple genetic causation of common, debilitating disease.” Lately the allure of a simple story thwarts public understanding of another technology that’s grabbed the spotlight in the wake of the genetic data boom:  artificial intelligence (AI).  With AI, headlines often focus on the ability of machines to “beat” doctors at finding disease. Take coverage of a study published this month on a Google algorithm for reading mammograms: CNBC: Google’s DeepMind A.I.

At least anecdotally, Harvey said, some young doctors are eschewing the field of radiology in the UK, where there is a shortage.  Harvey drew chuckles during a speech at the Radiological Society of North American in December when he presented a slide showing that while about 400 AI companies has sprung up in the last five years, the number of radiologists who have lost their jobs stands at zero.

(Medium ran Harvey’s defiant explanation of why radiologists won’t easily be nudged aside by computers.) The human-versus-machine fixation distracts from questions of whether AI will benefit patients or save money.  We’ve often written about the pitfalls of reporting on drugs that have only been studied in mice.

Almost always, a computer’s “deep learning” ability is trained and tested on cleaned-up datasets that don’t necessarily predict how they’ll perform in actual patients.  Harvey said there’s a downside to headlines “overstating the capabilities of the technology before it’s been proven.” “I think patients who read this stuff can get confused.

In Undark, Jeremy Hsu reported on the lack of evidence for a triaging app, Babylon Health.  Harvey said journalists also need to point out “the reality of what it takes to get it into the market and into the hands of end users.” He cites lung cancer screening, for which some stories cover “how good the algorithm is at finding lung cancers and not much else.” For example, a story that appeared in the New York Post (headline: “Google’s new AI is better at detecting lung cancer than doctors”)  declared that “AI is proving itself to be an incredible tool for improving lives” without presenting any evidence.

Can mobile health apps replace GPs? A scoping review of comparisons between mobile apps and GP tasks

This scoping review identified mobile apps that are capable of performing GP tasks.

mHealth apps serve diverse purposes and perform a range of functions for both patients and health care providers [26, 27].

Previous studies have reported the roles of mHealth apps for patients with specific clinical goals, such as pain self-management and weight management [28, 29].

Such mobile app functions may replace several GP tasks, for example, an app for diagnosis could help users make decisions regarding further treatment options, therefore potentially replacing a GP for this purpose.

Examples of technologies capable of supporting mHealth approaches include near-field communication (NFC) (a short-range, wireless connectivity technology), accelerometers (a technology used to measure gravitational forces and accelerations), gyroscopes (a micro-electromechanical system sensor used to measure body movement), artificial intelligence (AI), and machine learning [30–32].

The other competencies of GPs comprise personal attributes, including attitudes, practical skills, and soft skills, which mHealth apps cannot currently replace.

For example, AI and machine learning may enable machines to learn essential skills, as well as develop attitudes and a mindset similar to those of a good doctor.

A study explored doctor-patient communication through screen-to-screen versus face-to-face consultations showed no significantly different results regarding the quality of doctor-patient communication [38].

mHealth apps may additionally present several risks to the user, including loss of privacy, poor-quality patient data, and inappropriate clinical management of the user [39].

To address these risks, basic standards should be met, including accessibility, appropriate privacy, accuracy and credibility of content, and ethical obligations [40, 41].

This reflected a characteristic of scoping review, which primarily focused on identifying knowledge gaps and key characteristics related to a concept [44].

Opinion: AI, privacy and APIs will mold digital health in 2020 | MobiHealthNews

These trends and others are going to make a big impact in the healthcare space, especially as providers, payers and consumers alike slowly but surely recognize the need to incorporate tech into their workflows to meet the growing consumer demand for digital health tools.

AI in digital health has evolved by leaps and bounds.  In 2020, we will continue to see AI and ML push boundaries, while at the same time mature and settle into more defined patterns.   With the adoption of technologies like FaceID, facial recognition technology will be an important player in privacy and security — intimate concerns of the healthcare field.

While robotic primary care is a long way off, some view AI as a rival to more niche clinical positions.  The progress and traction of AI and ML raise lots of questions: can algorithms predict risk of sepsis better than trained ICU clinicians?

2019 witnessed the fallout of the Cambridge Analytica scandal, and added several high profile data concerns of its own: Amazon workers paid to listen to Alexa recordings, for example, and the transfer of non-deidentified, personal health data of more than 50 million Americans to Google.  As the current generation — fueled by smartphones, smart speakers, smart homes, smart everything — wakes up to the serious challenges to privacy that these technological efficiencies are potentially introducing, they’re educating themselves about data sharing and becoming more cautious about the information that they are potentially sharing with third-party sites.  For companies that deal with special categories of sensitive data — like medical information — the stakes are much higher.

In 2020, look for digital healthcare to establish increasingly tight security, clearly communicate privacy policies and provide more transparency around data use.  Interoperability is a major player in health tech innovation: patients will always receive care across multiple venues, and secure data exchange is key to providing continuity of care.

It’s a position that Apple has deliberately cultivated: as smartphone innovation falls into stalemate, they’re reaching toward bigger horizons — in Tim Cook’s words, improving health will be “Apple’s greatest contribution to mankind.”  These trends in digital health are not new.

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