Top 12 Disruptive Healthcare AI Technologies Announced
Partners HealthCare announced its selections for the fifth annual “Disruptive Dozen,” the 12 emerging artificial intelligence (AI) technologies with the greatest potential to impact healthcare in the next year. The technologies were featured as part of the World Medical Innovation Forum held in Boston to examine AI in clinical care including a range of diseases and health system opportunities.
“Understanding state-of-the-art medical technologies enables us to anticipate the future of clinical care,” said Gregg Meyer, MD, chief clinical officer, Partners HealthCare and 2019 World Forum co-chair. “The Disruptive Dozen technologies can offer physicians and patients a renewed sense of optimism about Artificial Intelligence and its impact on diagnosis and treatment.”
The 2019 Partners HealthCare Disruptive Dozen are:
1 Reimagining medical imaging – AI is transforming radiology and imaging, including mammography and ultrasound, to bring improvements in clinical care and diagnoses to patients worldwide. Researchers envision AI transforming mammography from one-size-fits-all to a more targeted tool for assessing breast cancer risk, and further increasing utility for ultrasound for disease detection and rapid acquisition of clinical-grade images.
2 Better prediction of suicide risk – Suicide is the 10th leading cause of death in the U.S. and the second leading cause of death among young people. AI is proving powerful in helping identify patients at risk of suicide (based on EHR data,) and also examining social media content with the goal of detecting early warning signs of suicide. These efforts toward an early warning system could help alert physicians, mental health professionals and family members when someone in their care needs help. These technologies are under development and not cleared for clinical use.
3 Streamlining diagnosis – The application of AI in clinical workflows such as imaging and pathology is ushering in a new era of AI-enabled disease diagnosis. From identifying abnormal and potentially life-threatening findings in medical imaging, to screening pathology cases according to the presence of urgent findings such as cancer cells, AI is poised to aid the diagnostic, prognostic, and treatment decisions that clinicians make while caring for patients.
4 Automated malaria detection — Nearly half a million people succumbed to malaria in 2017, with the majority being children under five. Deep learning technologies are helping automate malaria diagnosis, with software to detect and quantify malaria parasites with 90 percent accuracy and specificity. Such an automated approach to malaria detection and diagnosis could benefit millions of people worldwide by helping to deliver more accurate and timely diagnoses and could enable better monitoring of treatment efficacy.
5 Real-time monitoring and analysis of brain health – a window on the brain – A new world of real-time monitoring of the brain promises to dramatically improve patient care. By automating the manual and painstaking analysis of EEGs and other high-frequency wave forms, clinicians can rapidly detect electrical abnormalities that signal trouble. Deep learning algorithms based on terabytes of EEG data are helping to automatically detect seizures in the critically ill, regardless of the underlying cause of illness.
6 “A-Eye”: Artificial intelligence for eye health and disease – Not only is AI is helping advance new approaches in ophthalmology, it’s demonstrating the ability of AI-enabled technologies to enhance primary care with specialty level diagnostics. In 2018, the Food and Drug Administration approved a new AI-based system for the detection of diabetic retinopathy, marking the first fully automated, AI-based diagnostic tool approved for market in the U.S. that does not require additional expert review. The technology could also play a role in low-resource settings, where access to ophthalmologic care may be limited.
7 Lighting a “FHIR” under health information exchange — A new data standard, known as the Fast Healthcare Interoperability Resources (FHIR) has become the de facto standard for sharing medical and other health-related information. With its modern, web-based approach to health information exchange, FHIR promises to enable a new world of possibilities rooted in patient-centered care. While this new world is just emerging, it promises to give patients unfettered access to their own health information — allowing them to decide what they want to share and with whom and demanding careful consideration of data privacy and security.
8 Reducing the burden of healthcare administration — use of AI to automate routine and highly repetitious administrative functions. In the U.S., more than 25 percent of healthcare expenditures are due to administrative costs, far surpassing all other developed nations. One important area where AI could have a sizeable impact is medical coding and billing, where AI can develop automated approaches. The goal is to help reduce the complexity of the coding and billing process thereby reducing the number of mistakes and minimize the need for intense regulatory oversight.
9 A revolution in acute stroke care — Stroke is a major cause of death and disability across the world and a significant source of healthcare spending. Each year in the U.S., nearly 800,000 people suffer from a stroke, with a cost of roughly $34 billion. AI tools to help automate the diagnostic journey of ischemic stroke can help determine whether there is bleeding within the brain — a crucial early insight that helps doctors select the proper treatment. These algorithms can automatically review a patient’s head CT scan to identify a cerebral hemorrhage as well as help localize its source and determine the volume of brain tissue affected.
10 The hidden signs of intimate partner violence – Researchers are working to develop AI-enabled tools that can help alert clinicians if a patient’s injuries likely stem from intimate partner violence (IPV). Through an AI-enabled system, they hope to help break the silence that surrounds IPV by empowering clinicians with powerful, data-driven tools. While screening for intimate partner violence (IPV) can help detect and prevent future violence, less than 30 percent of IPV cases seen in the ER are appropriately flagged as abuse-related. Healthcare providers are optimistic that AI tools will further complement their role as a trusted source for divulging abuse.