AI News, When AI is used in medicine patients will need new protections

When AI is used in medicine patients will need new protections

For Elon Musk, the term artificial intelligence conjures apocalyptic scenarios of autonomous robots wreaking destruction in a world dominated by hyper-intelligent machines.

Stephen Hawking foresees a future in which smart machines replace sluggish humans across a range of activities, driving millions out of work.

Despite its title, he told an audience in Davos last month that artificial intelligence would be a force for good, narrowing the wealth gap between rich and poor countries.

Rural dwellers, living far from medical facilities, may be able to enjoy the same level of expertise as their urban counterparts and, ultimately, those in low income countries may benefit from the same expert input as those in the industrialised world.

As a surgeon and researcher I was dismayed by the revelations last month that William E Wecker Associates, a company working for the tobacco industry, obtained the lung cancer records of almost 180,000 patients from Public Health England.

The NHS has a unique store of millions of medical records providing an unparalleled resource from which, with the use of digital techniques, we may speed progress to the next breakthroughs in medical science and transform care.

The challenge, then, is to devise a system of data governance that protects the interests of patients, provides access for researchers, distributes the fruits of success fairly and wins the confidence of the public.

But unless we establish clear rules from the outset we risk sacrificing public trust, surrendering vital clinical gains and squandering the potential in the vast quantities of medical data we have spent decades accumulating.

Researchers say use of artificial intelligence in medicine raises ethical questions

David Magnus, PhD, senior author of the piece and director of the Stanford Center for Biomedical Ethics, said bias can play into health data in three ways: human bias;

What if different treatment decisions about patients are made depending on insurance status or their ability to pay?” The authors called for a national conversation about the “perpetual tension between the goals of improving health and generating profit … since the builders and purchasers of machine-learning systems are unlikely to be the same people delivering bedside care.” They also put the responsibility for finding solutions and setting the agenda on health care professionals.

Remaining ignorant about the construction of machine-learning systems or allowing them to be constructed as black boxes could lead to ethically problematic outcomes.” The authors acknowledge the social pressure to incorporate the latest tools in order to provide better health outcomes for patients.

Big Data and Artificial Intelligence Will Revolutionize our Lives

There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.

In 2013, it encompassed 4.4 zettabytes, however by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes (!).

We have not yet reached the state of “real” AI, but it is ready to sneak into our lives without any great announcement or fanfares – narrow AI is already in our cars, in Google searches, Amazon suggestions and in many other devices.

Apple’s Siri, Microsoft’s Cortana, Google’s OK Google, and Amazon’s Echo services are nifty in the way that they extract questions from speech using natural-language processing and then do a limited set of useful things, such as look for a restaurant, get driving directions, find an open slot for a meeting, or run a simple web search.

However, these solutions will only revolutionize medicine and healthcare if they are available to the average, mainstream users – and not only to the richest medical institutions (because they are too expensive) or to a handful of experts (because they are too difficult to use).

Artificial intelligence already found several areas in healthcare to revolutionize starting from the design of treatment plans through the assistance in repetitive jobs to medication management or drug creation.

Recently, the AI research branch of the search giant, Google, launched its Google Deepmind Health project, which is used to mine the data of medical records in order to provide better and faster health services.

The medical start-up, Enlitic, which also aims to couple deep learning with vast stores of medical data to advance diagnostics and improve patient outcomes, formulated the perks of deep learning the following way: “Until recently, diagnostic computer programs were written using a series of predefined assumptions about disease-specific features.

The British subscription, online medical consultation and health service, Babylon launched an application this year which offers medical AI consultation based on personal medical history and common medical knowledge.

The AiCure app supported by The National Institutes of Health uses a smartphone’s webcam and AI to autonomously confirm that patients are adhering to their prescriptions, or with better terms, supporting them to make sure they know how to manage their condition.

They are inventing a new generation of computational technologies that can tell doctors what will happen within a cell when DNA is altered by genetic variation, whether natural or therapeutic.

Another great example of using big data for patient management is Berg Health, a Boston-based biopharma company, which mines data to find out why some people survive diseases and thus improve current treatment or create new therapies.

They combine AI with the patients’ own biological data to map out the differences between healthy and disease-friendly environments and help in the discovery and development of drugs, diagnostics and healthcare applications.

An open AI ecosystem refers to the idea that with an unprecedented amount of data available, combined with advances in natural language processing and social awareness algorithms, applications of AI will become increasingly more useful to consumers.

This huge amount of data could be analyzed in details not only to provide patients who want to be proactive with better suggestions about lifestyle, but it could also serve healthcare with instructive pieces of information about how to design healthcare based on the needs and habits of patients.

They can tell if a doctor, clinic or hospital makes mistakes repetitively in treating a certain type of condition in order to help them improve and avoid unnecessary hospitalizations of patients.

We need the following preparations to avoid the pitfalls of the utilization of AI: If we succeed, huge medical discoveries and treatment breakthroughs will dominate the news not from time to time, but several times a day.

How Robots Are Changing the Way You See a Doctor

While any doctor will quickly credit her rigorous medical training in the nuts and bolts of how the human body works, she will just as adamantly school you on how virtually all of the decisions she makes—about how to diagnose disease and how best to treat it—are equally the product of some less tangible measures: her experience from previous patients;

Machine learning, the most basic form of artificial intelligence, is already infiltrating the medical field, and it turns out that machines can play an important role in improving our health—including making diagnoses more accurately and quickly and finding better treatments that save people time and money and prevent exposure to harmful side effects.

In fact, with modern medicine increasingly dependent on large numbers of studies and drug options and reams of new information, machines may be better able to keep up with and interpret data than the human mind.

programs take the amassed knowledge that every good physician has—which is the product of everything she learned in medical school and in training as well as her experience in treating patient after patient—and scale it to unprecedented levels.

“The way artificial intelligence starts to really impact what’s going on in health care is to be able to start cloning all the expert knowledge, so now all of a sudden you get access to all types of care, anywhere,”

And with the amount of data available to physicians today—from information about disease symptoms to new drugs, interactions between different drugs and how different people treated in the same way can have very different outcomes—the ability to access and digest information is fast becoming a required skill.

And in the mental-health field, startups are jumping into machine-learning apps that can help detect when people with conditions such as depression or bipolar disorder are on the verge of a new episode of symptoms in a way that no psychiatrist, however dedicated, ever could.

Watson provides access to a database of the collected knowledge of Memorial Sloan Kettering’s cancer doctors, as well as the most important cancer studies in the medical literature that these doctors rely on when making their decisions about how to treat patients.

These range from current standard therapies that have already been approved for that type of cancer to treatments approved for other cancers that are currently being tested but are not yet approved for the patient’s specific cancer, and finally truly experimental treatments that some early studies hint might be effective at treating the disease.

People with rarer cancers that their local physicians haven’t treated before won’t have to travel great distances to a major hospital that has more experience with that disease, or have to miss the opportunity to get their cancer treated at all.

As more information about different cancer patients and their tumors becomes part of Watson, doctors will be able to see patterns that will help them match specific patient profiles to survival rates and better outcomes.

The app, once installed on a smartphone, monitors activity on social media and phone calls to discern patterns of communication so that when depressive episodes strike, for example, and those patterns change, the app will detect it.

“Potentially, with technology like Cogito, we may be able to develop an early-warning system that, for somebody who has a high risk profile because they have a history of depression or suicide attempts, could monitor and see changes in patterns to better determine when the risk gets to the level where intervention is needed to prevent episodes of self-harm or dangerous activity.

7 Applications of Machine Learning in Pharma and Medicine

When it comes to effectiveness of machine learning, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning  in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.

Image credit: Google DeepMind Health – An OCT scan of one of the DeepMind Health team’s eyes In the area of brain-based diseases like depression, Oxford’s P1vital® Predicting Response to Depression Treatment (PReDicT) project is using predictive analytics to help diagnose and provide treatment, with the overall goal of producing a commercially-available emotional test battery for use in clinical settings.

IBM Watson Oncology is a leading institution at the forefront of driving change in treatment decisions, using patient medical information and history to optimize the selection of treatment options: IBM Watson and Memorial Sloan Kettering Over the next decade, increased use of micro biosensors and devices, as well as mobile apps with more sophisticated health-measurement and remote monitoring capabilities, will provide another deluge of data that can be used to help facilitate R&D and treatment efficacy.

Image credit: Circulation – A: Matrix representation of the supervised and unsupervised learning problem B: Decision trees map features to outcome. C: Neural networks predict outcome based on transformed representations of features D: The k-nearest neighbor algorithm assigns class based on the values of the most similar training examples Key players in this domain include the MIT Clinical Machine Learning Group, whose precision medicine research is focused on the development of algorithms to better understand disease processes and design for effective treatment of diseases like Type 2 diabetes. Microsoft’s Project Hanover is using ML technologies in multiple initiatives, including a collaboration with the Knight Cancer Institute to develop AI technology for cancer precision treatment, with a current focus on developing an approach to personalize drug combinations for Acute Myeloid Leukemia (AML).

Applying advanced predictive analytics in identifying candidates for clinical trials could draw on a much wider range of data than at present, including social media and doctor visits, for example, as well as genetic information when looking to target specific populations;

Document classification (sorting patient queries via email, for example) using support vector machines, and optical character recognition (transforming cursive or other sketched handwriting into digitized characters), are both essential ML-based technologies in helping advance the collection and digitization of electronic health information.

MATLAB’s ML handwriting recognition technologies and Google’s Cloud Vision API for optical character recognition are just two examples of innovations in this area: The MIT Clinical Machine Learning Group is spearheading the development of next-generation intelligent electronic health records, which will incorporate built-in ML/AI to help with things like diagnostics, clinical decisions, and personalized treatment suggestions.

MIT notes on its research site the “need for robust machine learning algorithms that are safe, interpretable, can learn from little labeled training data, understand natural language, and generalize well across medical settings and institutions.”

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