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12 ways AI will revolutionize healthcare in the next year<\/h2>
Expectations are high for the impact that artificial intelligence can play in healthcare, but most experts admit that the industry is still early in the process of researching one-off uses for AI in healthcare settings. Still, many predict that many opportunities will soon emerge to take full advantage of advanced computing technologies.Recently, faculty members of Partners HealthCare ranked artificial intelligence-enabled technologies that will have the greatest impact on medicine in the next 12 months. More than 60 in-person and telephone interviews were conducted with Partners HealthCare faculty to nominate the breakthrough innovations. To be considered for the annual ranking, nominated healthcare innovations had to have strong potential for significant clinical impact and patient benefit in comparison to current medical practices, as well as be on the market by 2020.<\/p>

Reimagining medical imaging<\/h2>
Researchers are using AI-based\u00a0approaches to increase the power of mammography, transforming it to a more targeted tool for assessing breast cancer risk. For example, a team in Massachusetts is leveraging machine learning in multiple ways to improve breast cancer screening. The researchers developed an AI-based method, now in clinical use at a large hospital for slightly more than a year that can automatically determine breast density\u00a0using mammograms.<\/p>

Improving predictions of suicide risk<\/h2>
Researchers in Washington, Virginia and other states,\u00a0as well as teams based at major social media companies, are using natural language processing and machine learning methods to create algorithms that can detect the early warning signs of suicide. Such tools could form the basis of an app or other technology-based system that parents, other caregivers and medical providers can use to alert them when an adolescent in their care is contemplating suicide.<\/p>

Streamlining diagnosis<\/h2>
Clinical imaging has become\u00a0increasingly digitized, paving the way for AI-based approaches that could bring efficiencies to clinical operations and decision making. For example, AI offers the prospect of prioritizing patients\u2019 images for analysis, moving them to the top of the virtual stack for radiologists, based on the likelihood of an abnormal and potentially life-threatening finding.\u00a0For example, researchers in California\u00a0recently developed a deep-learning-based algorithm that can distinguish normal chest X-ray images from abnormal ones.<\/p>

Automating malaria detection<\/h2>
With the help of deep learning methods, a research group in\u00a0Washington has developed an approach that automates malaria diagnosis. Their tool can detect and quantify malaria parasites with 90 percent accuracy and specificity, matching the level of performance of human experts. Researchers packaged their algorithm within an inexpensive automated digital microscope. The system has yielded impressive results in field tests in Thailand and Peru, and it\u2019s now undergoing more testing and is in commercial development.<\/p>

Offering a window on the brain<\/h2>
Progress is being made toward real-time monitoring and analysis of brain health. For example, a team of researchers\u00a0in Boston has annotated some 30 terabytes of EEG data from thousands of patients, and they\u2019ve mined the data to create deep learning algorithms that can automatically detect seizures in the critically ill, regardless of the underlying cause of illness. These tools are now under development and will be deployed in the coming year at a large hospital in the region, making real-time analysis of EEG a reality.<\/p>

Using AI for eye health and disease<\/h2>
In 2018, the Food and Drug Administration\u00a0took a historic step with the approval of a new AI-based system for the detection of diabetic retinopathy.\u00a0Researchers based in Iowa recently examined the clinical use of this tool\u00a0for diabetic retinopathy through\u00a0a large prospective analysis.\u00a0Meanwhile, research teams in the United Kingdom have been working to design AI-based tools that are more generalizable and not geared to a single eye disease, but able to detect within a single imaging modality features used in the diagnosis of more than 50 common eye conditions, including age-related macular degeneration.<\/p>

Amplifying FHIR\u2019s use in health information exchange<\/h2>
A new data standard, known as the Fast Healthcare Interoperability\u00a0Resources (FHIR) is gaining popularity and has become the de facto standard for sharing medical and other health-related information. With its web-based approach to health information exchange, FHIR promises to enable a new world of possibilities rooted in patient-centered care. Perhaps the most trailblazing aspect of\u00a0FHIR is the new realm of patient-controlled data sharing it will create.<\/p>

Reducing the burden of healthcare administration<\/h2>
AI could have a sizeable impact on medical coding\u00a0and billing. To help streamline the process of medical coding, teams across the country are harnessing AI to develop automated approaches. For example, a Boston-based startup has created a suite of machine learning algorithms that can analyze the doctors\u2019 notes from patients\u2019 electronic health records and automatically generate the proper diagnostic and procedure codes.<\/p>

Enabling a revolution in acute stroke care<\/h2>
Massachusetts researchers have developed a set of AI-based algorithms that can help determine whether or not there is bleeding within the brain. The algorithms, which were trained on some 2,000 head CT images, can automatically review a patient\u2019s head CT scan to identify a cerebral hemorrhage, help localize its source and determine the volume of brain tissue that is affected. Currently, the team is working to expand these tools by creating additional AI-based methods that similarly automate the\u00a0subsequent steps of ischemic stroke diagnosis.<\/p>

Narrowing gaps in mental healthcare<\/h2>
Research teams across the country\u00a0are harnessing technology, including AI-based methods, to help narrow the gaps in mental healthcare. For example, researchers in\u00a0Massachusetts are developing an app for patients with opioid, alcohol and other forms of drug addiction, which provides a virtual form of integrated group therapy, a highly effective treatment that teaches patients behaviors and skills to manage their own recovery and prevent relapse.<\/p>

Bringing voice-first technology to healthcare<\/h2>
AI-powered voice-first technology is coming to the clinic.\u00a0Using speech recognition and natural language processing, several vendors are developing tools that are designed to help clinicians deliver better care and also give them more quality time with patients. For example, a host of healthcare technology companies are working on voice assistants that can help reduce physicians\u2019 data entry burdens.<\/p>

Unmasking potential intimate partner violence<\/h2>
Researchers are working to develop AI-enabled tools that can\u00a0help alert clinicians if a patient\u2019s injuries likely stem from intimate partner violence\u00a0(IPV).\u00a0Their goal is to create an\u00a0integrated, AI-based system that can analyze a patient\u2019s clinical and radiological data and automatically alert radiologists and clinicians if\u00a0the injuries are likely to be associated with IPV. A\u00a0research team in Boston recently\u00a0conducted a pilot study that analyzed the electronic health records and imaging exams of nearly 200 patients with a known history of IPV as well as a control set of more than 550\u00a0non-abused patients.<\/p>

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12 ways AI will revolutionize healthcare in the next year<\/h2>
Expectations are high for the impact that artificial intelligence can play in healthcare, but most experts admit that the industry is still early in the process of researching one-off uses for AI in healthcare settings. Still, many predict that many opportunities will soon emerge to take full advantage of advanced computing technologies.Recently, faculty members of Partners HealthCare ranked artificial intelligence-enabled technologies that will have the greatest impact on medicine in the next 12 months. More than 60 in-person and telephone interviews were conducted with Partners HealthCare faculty to nominate the breakthrough innovations. To be considered for the annual ranking, nominated healthcare innovations had to have strong potential for significant clinical impact and patient benefit in comparison to current medical practices, as well as be on the market by 2020.<\/p>

Reimagining medical imaging<\/h2>
Researchers are using AI-based\u00a0approaches to increase the power of mammography, transforming it to a more targeted tool for assessing breast cancer risk. For example, a team in Massachusetts is leveraging machine learning in multiple ways to improve breast cancer screening. The researchers developed an AI-based method, now in clinical use at a large hospital for slightly more than a year that can automatically determine breast density\u00a0using mammograms.<\/p>

Improving predictions of suicide risk<\/h2>
Researchers in Washington, Virginia and other states,\u00a0as well as teams based at major social media companies, are using natural language processing and machine learning methods to create algorithms that can detect the early warning signs of suicide. Such tools could form the basis of an app or other technology-based system that parents, other caregivers and medical providers can use to alert them when an adolescent in their care is contemplating suicide.<\/p>

Streamlining diagnosis<\/h2>
Clinical imaging has become\u00a0increasingly digitized, paving the way for AI-based approaches that could bring efficiencies to clinical operations and decision making. For example, AI offers the prospect of prioritizing patients\u2019 images for analysis, moving them to the top of the virtual stack for radiologists, based on the likelihood of an abnormal and potentially life-threatening finding.\u00a0For example, researchers in California\u00a0recently developed a deep-learning-based algorithm that can distinguish normal chest X-ray images from abnormal ones.<\/p>

Automating malaria detection<\/h2>
With the help of deep learning methods, a research group in\u00a0Washington has developed an approach that automates malaria diagnosis. Their tool can detect and quantify malaria parasites with 90 percent accuracy and specificity, matching the level of performance of human experts. Researchers packaged their algorithm within an inexpensive automated digital microscope. The system has yielded impressive results in field tests in Thailand and Peru, and it\u2019s now undergoing more testing and is in commercial development.<\/p>

Offering a window on the brain<\/h2>
Progress is being made toward real-time monitoring and analysis of brain health. For example, a team of researchers\u00a0in Boston has annotated some 30 terabytes of EEG data from thousands of patients, and they\u2019ve mined the data to create deep learning algorithms that can automatically detect seizures in the critically ill, regardless of the underlying cause of illness. These tools are now under development and will be deployed in the coming year at a large hospital in the region, making real-time analysis of EEG a reality.<\/p>

Using AI for eye health and disease<\/h2>
In 2018, the Food and Drug Administration\u00a0took a historic step with the approval of a new AI-based system for the detection of diabetic retinopathy.\u00a0Researchers based in Iowa recently examined the clinical use of this tool\u00a0for diabetic retinopathy through\u00a0a large prospective analysis.\u00a0Meanwhile, research teams in the United Kingdom have been working to design AI-based tools that are more generalizable and not geared to a single eye disease, but able to detect within a single imaging modality features used in the diagnosis of more than 50 common eye conditions, including age-related macular degeneration.<\/p>

Amplifying FHIR\u2019s use in health information exchange<\/h2>
A new data standard, known as the Fast Healthcare Interoperability\u00a0Resources (FHIR) is gaining popularity and has become the de facto standard for sharing medical and other health-related information. With its web-based approach to health information exchange, FHIR promises to enable a new world of possibilities rooted in patient-centered care. Perhaps the most trailblazing aspect of\u00a0FHIR is the new realm of patient-controlled data sharing it will create.<\/p>

Reducing the burden of healthcare administration<\/h2>
AI could have a sizeable impact on medical coding\u00a0and billing. To help streamline the process of medical coding, teams across the country are harnessing AI to develop automated approaches. For example, a Boston-based startup has created a suite of machine learning algorithms that can analyze the doctors\u2019 notes from patients\u2019 electronic health records and automatically generate the proper diagnostic and procedure codes.<\/p>

Enabling a revolution in acute stroke care<\/h2>
Massachusetts researchers have developed a set of AI-based algorithms that can help determine whether or not there is bleeding within the brain. The algorithms, which were trained on some 2,000 head CT images, can automatically review a patient\u2019s head CT scan to identify a cerebral hemorrhage, help localize its source and determine the volume of brain tissue that is affected. Currently, the team is working to expand these tools by creating additional AI-based methods that similarly automate the\u00a0subsequent steps of ischemic stroke diagnosis.<\/p>

Narrowing gaps in mental healthcare<\/h2>
Research teams across the country\u00a0are harnessing technology, including AI-based methods, to help narrow the gaps in mental healthcare. For example, researchers in\u00a0Massachusetts are developing an app for patients with opioid, alcohol and other forms of drug addiction, which provides a virtual form of integrated group therapy, a highly effective treatment that teaches patients behaviors and skills to manage their own recovery and prevent relapse.<\/p>

Bringing voice-first technology to healthcare<\/h2>
AI-powered voice-first technology is coming to the clinic.\u00a0Using speech recognition and natural language processing, several vendors are developing tools that are designed to help clinicians deliver better care and also give them more quality time with patients. For example, a host of healthcare technology companies are working on voice assistants that can help reduce physicians\u2019 data entry burdens.<\/p>

Unmasking potential intimate partner violence<\/h2>
Researchers are working to develop AI-enabled tools that can\u00a0help alert clinicians if a patient\u2019s injuries likely stem from intimate partner violence\u00a0(IPV).\u00a0Their goal is to create an\u00a0integrated, AI-based system that can analyze a patient\u2019s clinical and radiological data and automatically alert radiologists and clinicians if\u00a0the injuries are likely to be associated with IPV. A\u00a0research team in Boston recently\u00a0conducted a pilot study that analyzed the electronic health records and imaging exams of nearly 200 patients with a known history of IPV as well as a control set of more than 550\u00a0non-abused patients.<\/p>

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12 ways AI will revolutionize healthcare in the next year

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