AI News, Shape Created with Sketch. In pictures: Artificial intelligence through history

Shape Created with Sketch. In pictures: Artificial intelligence through history

AI has the potential to provide more precise, personalised care, as well as help us to shift our focus from treatment to prevention and tackle some of the world’s biggest global health issues.

The WHO estimates that achieving the health-related targets under the Sustainable Development Goals – from ending tuberculosis to ensuring universal access to sexual and reproductive healthcare services by 2030 – will cost between $134bn-$371bn(£97bn-£270bn)a year over current health spending.

And in low-income countries where mortality rates from car accidents are the highest, the deep neural networks in self-driving cars improve road safety.

A lack of information on the most vulnerable communities – minorities, the poor, those living in rural regions and in emergencies – biases systems.

It is essential to create the infrastructure to collect, centralise and construct equitable datasets ensuring that every country, community and person counts.

UN Global Pulse, an initiative that is working to incorporate the newest technology within the development and humanitarian sectors, analysed radio content in Uganda using machine learning to distilpatterns ofmalaria outbreaks, the leading cause of death in the country.

An example is Partnership on AI, which includes prominent tech companies and research institutions alongside a handful of non-profits like Unicef, Human Rights Watch, and the ACLU working to “ensure that applications of AI are beneficial to people and society”.

We need a sustainable, multi-stakeholder ecosystem, working globally and locally, to build the foundational safeguards to maximise the value generated by AI for the whole of society.

How Is AI Used In Healthcare - 5 Powerful Real-World Examples That Show The Latest Advances

When it comes to our health, especially in matters of life and death, the promise of artificial intelligence (AI) to improve outcomes is very intriguing.

While there is still much to overcome to achieve AI-dependent health care, most notably data privacy concerns and fears of mismanaged care due to machine error and lack of human oversight, there is sufficient potential that governments, tech companies, and healthcare providers are willing to invest and test out AI-powered tools and solutions.

AI-assisted robotic surgery With an estimated value of $40 billion to healthcare, robots can analyze data from pre-op medical records to guide a surgeon's instrument during surgery, which can lead to a 21% reduction in a patient's hospital stay.

Most applications of virtual nursing assistants today allow for more regular communication between patients and care providers between office visits to prevent hospital readmission or unnecessary hospital visits.

Image analysis Currently, image analysis is very time consuming for human providers, but an MIT-led research team developed a machine-learning algorithm that can analyze 3D scans up to 1,000 times faster than what is possible today.

Additionally, AI image analysis could support remote areas that don’t have easy access to healthcare providers and even make telemedicine more effective as patients can use their camera phones to send in pics of rashes, cuts or bruises to determine what care is necessary.

In the very complex world of healthcare, AI tools can support human providers to provide faster service, diagnose issues and analyze data to identify trends or genetic information that would predispose someone to a particular disease.

Shape Created with Sketch. In pictures: Artificial intelligence through history

AI has the potential to provide more precise, personalised care, as well as help us to shift our focus from treatment to prevention and tackle some of the world’s biggest global health issues.

The WHO estimates that achieving the health-related targets under the Sustainable Development Goals – from ending tuberculosis to ensuring universal access to sexual and reproductive healthcare services by 2030 – will cost between $134bn-$371bn(£97bn-£270bn)a year over current health spending.

And in low-income countries where mortality rates from car accidents are the highest, the deep neural networks in self-driving cars improve road safety.

A lack of information on the most vulnerable communities – minorities, the poor, those living in rural regions and in emergencies – biases systems.

It is essential to create the infrastructure to collect, centralise and construct equitable datasets ensuring that every country, community and person counts.

UN Global Pulse, an initiative that is working to incorporate the newest technology within the development and humanitarian sectors, analysed radio content in Uganda using machine learning to distilpatterns ofmalaria outbreaks, the leading cause of death in the country.

An example is Partnership on AI, which includes prominent tech companies and research institutions alongside a handful of non-profits like Unicef, Human Rights Watch, and the ACLU working to “ensure that applications of AI are beneficial to people and society”.

We need a sustainable, multi-stakeholder ecosystem, working globally and locally, to build the foundational safeguards to maximise the value generated by AI for the whole of society.

Machine Learning Healthcare Applications – 2018 and Beyond

In the broad sweep of AI’s current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years.

Since early 2013, IBM’s Watson has been used in the medical field, and after winning an astounding series of games against with world’s best living Go player, Google DeepMind‘s team decided to throw their weight behind the medical opportunities of their technologies as well.

We’ve written this article, not to be a complete catalogue of possible applications, but to highlight a number of current and future uses of machine learning in the medical field, with relevant links to external sources and related TechEmergence interviews.

Microsoft’s InnerEye initiative (started in 2010) is presently working on image diagnostic tools, and the team has posted a number of videos explaining their developments, including this video on machine learning for image analysis: Deep learning will probably play a more and more important role in diagnostic applications as deep learning becomes more accessible, and as more data sources (including rich and varied forms of medical imagery) become part of the AI diagnostic process.

MSK has reams of data on cancer patients and treatments used over decades, and it’s able to present and suggest treatment ideas or options to doctors in dealing with unique future cancer cases –

IBM is going to great lengths to acquire all the health data it can get its hands on, from partnering with Medtronic to make sense of diabetes and insulin data in real time, to buying out healthcare analytics company Truven Health for $2.6B.

While much of the healthcare industry is a morass of laws and criss-crossing incentives of various stakeholders (hospital CEOs, doctors, nurses, patients, insurance companies, etc…), drug discovery stands out as a relatively straightforward economic value for machine learning healthcare application creators.

This device allows surgeons to manipulate dextrous robotic limbs in order to perform surgeries with fine detail and in tight spaces (and with less tremors) than would be possible by the human hand alone.

Here’s a video highlighting the incredible dexterity of the Da Vinci robot: While not all robotic surgery procedures involve machine learning, some systems use computer vision (aided by machine learning) to identify distances, or a specific body part (such as identifying hair follicles for transplantation on the head, in the case of hair transplantation surgery).

The promise of personalized medicine is a world in which everyone’s health recommendations and disease treatments are tailored based on their medical history, genetic lineage, past conditions, diet, stress levels, and more.

giving someone a slightly lesser dose of Bactrim for a UTI, or a completely unique variation of Bactrim formulated to avoid side effects for a person with a specific genetic profile), it is likely to make much of its initial impact in high-stakes situations (i.e.

In the diabetes video created by Medtronic and IBM (visible here), Medtronic’s own Hooman Hakami states that at some point, Medtronic wants to have their insulin checking pumps work autonomously, monitoring blood-glucose levels and injecting insulin as needed, without disturbing the user’s daily life.

While western medicine has kept its primary focus on treatment and amelioration of disease, there is a great need for proactive health prevention and intervention, and the first wave of IoT devices (notably the Fitbit) is pushing these applications forward.

Machines have recently developed the ability to model beyond-human expertise in some kinds of visual art and painting: If a machine can be trained to replicate the legendary creative capacity of Van Gough or Picaso, we might imagine that with enough training, such a machine could “drink in”

Surely there is opportunity, but there are also unique obstacles in the medical field that aren’t always present in other domains: The above challenges are no reason to stop innovating, and I’m sure there there are some clinicians who have their fingers crossed that more of the world’s data scientists and computer scientists will hone in on improving healthcare and medicine.

70+ Companies Driving the Future of Healthcare Technology

We need it to help clinicians work more efficiently and health systems improve their services, but most importantly, we need technology in order to give patients the best possible care.

The lack of technology in healthcare is not for lack of the technology itself—there are literally thousands of companies who have developed brilliant ways to improve clinician workflows and patient care. The problem is that healthcare uses legacy systems which aren’t compatible with today’s technology, and overhauling an entire industry to be more technologically advanced isn’t as easy as it sounds ( …and it doesn’t even sound easy in the first place).

The truth is that we’re in the middle of a healthcare technology revolution, and while that sounds grandiose, all it means is that we’re figuring out how to implement more advanced ways of taking care of people in an industry that isn’t designed to accept new tools easily.

iRhythm Technologies — iRhythm is a digital healthcare company redefining the way cardiac arrhythmias are clinically diagnosed by combining our wearable biosensing technology with cloud-based data analytics and machine- learning capabilities.

Zephyr Health — Zephyr Health’s technology is a unique combination of patented, machine learning algorithms that create predictive insights using global health data for every major treatment area using thousands of connected data sources – public, private and vendor.

is a healthcare information technology company committed to providing healthcare organizations with knowledge-enabled tools that empower them to track, manage, and automate key administrative and financial services.

Reflexion Health — Their mission is to transform healthcare delivery and redesign patient care, exploiting appropriate technology to educate, motivate, measure, manage, and report—all to ease the patient’s journey, amplify the clinician’s impact, and speed recovery.

iMedX — Through its continuously growing technological capabilities, iMedX offers a full suite of high-value revenue cycle management solutions including medical transcription and coding services, results-based consulting, education and training opportunities, and data analytics.

Pocared Diagnostics — Pocared Diagnostics developed P-1000™, a tool that uses the unique physical properties of intrinsic fluorescence to provide the fully automated, direct specimen, CULTURE-FREE Microbiology® and reagent-free solution for microorganism detection, identification, and enumeration.

Pieces Technologies — Pieces Tech reimagines the intersection of healthcare and technology buy building software that interprets patient data in real-time, transforming billions of points into warning tools that can save lives and strengthen communities inside and outside of hospital walls.

Evena Medical — Evena’s patented technology replaces the traditional system of vein finders — hands-on, unguided needle insertion by feel – with a modern-day guidance system that matches multispectral viewing with ultrasound to show veins precisely where they are in a crisp, clear, storable and shareable image.

Clinipace — Clinipace uses a proprietary, comprehensive technology platform to support patient enrollment, project management, site selection and management, data capture, data management, monitoring, and biostatistics to improve collaboration and data visibility.

Casenet — Casenet provides a highly scalable, flexible and extensible, enterprise care management platform which enables our customers to improve individual and population health through better care coordination, improved quality, and more efficient care delivery.

medCPU — medCPU captures the complete clinical picture from clinicians’ free-text notes, dictations, discharge summaries and structured documentation entered into any EMR and analyzes it against a growing library of best-practice content, generating real-time precise prompts for best care consideration.

Prematics —  Prematics developed ScriptTone a reliable and highly secure service empowers physicians at the point-of-care to prescribe the most clinically appropriate and cost-effective prescription. ScriptTone benefits all of the key industry stakeholders by driving patient safety, lowering costs, and streamlining fulfillment.

Cedar — The Cedar platform looks through millions of internal and external data points – versus a single data source like a credit score – to constantly optimize revenue cycle activities and better predict patient engagement.

HNI Healthcare — HNI Healthcare’s technology, consulting and management services align processes, protocols, communication, and clinical best practices within hospital-based medicine, helping medical systems identify and correct clinical and operational issues to gain financial strength.

Health Fidelity — Health Fidelity transforms risk adjustment by offering a combination of technology and expertise that enables organizations participating in Medicare Advantage, ACA commercial, Managed Medicaid, or Medicare ACO programs to transform an otherwise manual risk adjustment process into an integrated workflow.

PharmEasy — PharmEasy delivers medicine to people’s doorstep at a 20% discount within 24 hours of placing the order and additionally provide home-based diagnostic test services at more than 40% discount.

Cureatr — Cureatr’s notifications alert clinicians and payers in real-time on mobile or desktop applications when an attributed patient or member is receiving care anywhere within a region and delivers necessary information that is essential to reducing preventable hospitalizations and avoiding readmissions.

Fruit Street Health — Fruit Street is a telemedicine software product that is licensed to healthcare professionals which allows them to conduct HIPAA compliant video consultations with their patients and monitor their patients’ health, diet, and lifestyle using medical and wearable devices.

Truveris — The Truveris platform empowers clients – from the individual patient to pharmacy benefits stakeholders – with the tools to more effectively and efficiently manage the complexity and rising costs of prescription drugs today.

Created by physicians and developed in close collaboration with leading medical centers, REACH helps hospital networks and accountable care organizations achieve measurable improvements in their clinical, operational and financial performance.

Verisma Systems — Verisma’s helps healthcare organizations and providers streamline and automate their Release of Information (ROI) processes via a patented, cloud-based ROI System that automates workflows to improve turnaround times, reduce errors and drive down costs.

Bay Labs — Bay Labs brings deep learning advances to critical unsolved problems in healthcare, specifically in cardiovascular imaging and care to combat heart disease, the leading cause of death worldwide.

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