AI News, Shape Created with Sketch. UK news in pictures

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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.

Top 12 Ways Artificial Intelligence Will Impact Healthcare

From chronic diseases and cancer to radiology and risk assessment, there are nearly endless opportunities to leverage technology to deploy more precise, efficient, and impactful interventions at exactly the right moment in a patient’s care.

As payment structures evolve, patients demand more from their providers, and the volume of available data continues to increase at a staggering rate, artificial intelligence is poised to be the engine that drives improvements across the care continuum.

Learning algorithms can become more precise and accurate as they interact with training data, allowing humans to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes.

At the 2018 World Medical Innovation Forum (WMIF) on artificial intelligence presented by Partners Healthcare, a leading researchers and clinical faculty members showcased the twelve technologies and areas of the healthcare industry that are most likely to see a major impact from artificial intelligence within the next decade.

With the help of experts from across the Partners Healthcare system, including faculty from Harvard Medical School (HMS), moderators Keith Dreyer, DO, PhD, Chief Data Science Officer at Partners and Katherine Andriole, PhD, Director of Research Strategy and Operations at Massachusetts General Hospital (MGH), counted down the top 12 ways artificial intelligence will revolutionize the delivery and science of healthcare.

Using computers to communicate is not a new idea by any means, but creating direct interfaces between technology and the human mind without the need for keyboards, mice, and monitors is a cutting-edge area of research that has significant applications for some patients.

“By using a BCI and artificial intelligence, we can decode the neural activates associated with the intended movement of one’s hand, and we should be able to allow that person to communicate the same way as many people in this room have communicated at least five times over the course of the morning using a ubiquitous communication technology like a tablet computer or phone.”

Brain-computer interfaces could drastically improve quality of life for patients with ALS, strokes, or locked-in syndrome, as well as the 500,000 people worldwide who experience spinal cord injuries every year.

“If we want the imaging to give us information that we presently get from tissue samples, then we’re going to have to be able to achieve very close registration so that the ground truth for any given pixel is known.”

Succeeding in this quest may allow clinicians to develop a more accurate understanding of how tumors behave as a whole instead of basing treatment decisions on the properties of a small segment of the malignancy.

However, algorithm developers must be careful to account for the fact that disparate ethnic groups or residents of different regions may have unique physiologies and environmental factors that will influence the presentation of disease.

EHRs have played an instrumental role in the healthcare industry’s journey towards digitalization, but the switch has brought myriad problems associated with cognitive overload, endless documentation, and user burnout.

For the hospitals sitting on mountains of EHR data and not using them to the fullest potential, to industry that’s not creating smarter, faster clinical trial design, and for EHRs that are creating these data not to use them…that would be a failure.”

“We’re now getting to the point where we can do a better job of assessing whether a cancer is going to progress rapidly or slowly and how that might change how patients will be treated based on an algorithm rather than clinical staging or the histopathologic grade,”

Using artificial intelligence to enhance the ability to identify deterioration, suggest thatsepsisis taking hold, or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties.

“When we’re talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on –

Machine learning algorithms and their ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.

So whether we need to integrate data within one institution or across multiple institutions is going to be a key factor in terms of augmenting the patient population to drive the modeling process.”

Data quality and integrity issues, plus a mishmash of data formats, structured and unstructured inputs, and incomplete records have made it very difficult to understand exactly how to engage in meaningful risk stratification, predictive analytics, and clinical decision support.

EHR analytics have produced many successful risk scoring and stratification tools, especially when researchers employ deep learning techniques to identify novel connections between seemingly unrelated datasets.

However, patients tend to trust their physicians more than they might trust a big company like Facebook, he added, which may help to ease any discomfort with contributing data to large-scale research initiatives.

Continuing the theme of harnessing the power of portable devices, experts believe that images taken from smartphones and other consumer-grade sources will be an important supplement to clinical quality imaging –

Using smartphones to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects may be able to help underserved areas cope with a shortage of specialists while reducing the time-to-diagnosis for certain complaints.

Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers in to problems long before they might otherwise recognize the need to act.

“But if you have an AI algorithm and lots and lots of data from many patients, it’s easier to match up what you’re seeing to long term patterns and maybe detect subtle improvements that would impact your decisions around care.”

By powering a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivering care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.

Ethical & Policy Risks of Artificial Intelligence in Healthcare

Chatbots offer 24/7 free therapy, wearables monitor biometric data in real time, and robotic devices improve surgical outcomes.

Leveraging data from doctors visits, digital devices and wearables, AI systems are able to consider unique patient history, genetics, lifestyle, diet, environment, and even bacterial composition of the gut.

At Memorial Sloan Kettering Hospital, IBM Watson uses machine learning to learn to suggest personalized medical treatments based on analysis of scores of medical research, including a myriad of drug interactions, and treatment outcomes for patients with similar genetic makeup, background, and cancer strains.

This rise of AI ‘Healthtech’ is enabled by developments in machine learning algorithms, proliferation of digital and biometric data captured by digital devices, accelerating computing power, and advances in biological and medical sciences including in genomic sequencing.

Remote diagnostic applications allow users to upload photos of snake bites or skin cancers for real-time prediction of diagnoses and treatments, without having to travel distances to see a doctor.

Livecare, a Romania based startup, has developed a small wearable patch that, simply taped onto the chest, uses machine learning (specifically an LSTM recurrent neural network algorithm) to monitor and provide feedback for heart diseases.

Whereas the number of doctors is limited and public medical systems over-stretched, healthcare delivered over digital devices is scalable to reach millions of people.

PARO, a robotic seal effective with dementia patients in nursing homes in Japan Yet readers of Noah Yuval Harari’s Homo Deus are aware of a host of new technologies that, if accessible to an elite, can segment the human species in new ways.

biometrics, are used to train the machine learning algorithms behind new drug discovery and cures, more accurate diagnostics, and personalized treatments.

Meanwhile, biometric data collected from wearables can be hacked or sold to public or private sectors actors to target advertising or real and “fake news” for political or social campaigns.

Johnson’s Sedasys could effectively deliver anesthesia to patients for $150-$200, compared to $2,000 for an anesthesiologist, one of healthcare’s highest-paid specialties in the United States.  Should hospitals be required to offer patients the option of an AI diagnostic or treatment program that is statistically safer, more accurate, cheaper, or all of the above?

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