What are some of the leading providers of AI technology in health care?
by Phil Britt
Health care companies are using artificial intelligence (AI) to improve the diagnosis, treatment and care of patients. They’re providing AI software for a range of health care applications, such as analyzing data, interpreting images and summarizing medical notes. Here, we look at some of the top AI companies serving the health care market.
Top AI Companies in Health Care
1. Cleerly
Cleerly uses FDA-cleared machine learning (ML) algorithms to non-invasively measure atherosclerosis (plaque), stenosis and the likelihood of ischemia using coronary computed tomography angiography (CCTA) studies. The company’s AI generates a 3D model of the patient’s coronary arteries, identifies their lumen and vessel walls, locates and measures stenoses and quantifies and categorizes plaque. The measurements are provided to health care providers to support diagnosis and personalized treatment. Customers can also review results in-depth via Cleerly’s web platform.
2. Enlitic
Enlitic’s AI-powered software saves time for radiologists by reading clinical content and then uses machine learning to analyze and interpret the data. The company bought Laitek, a medical imaging data migration and routing services provider, to further enhance workflow for radiologists. Enlitic’s AI solution also works with data management applications to provide health care providers with improved administration, processing and sharing of patient data throughout the health care ecosystem.
3. Qure.ai
Qure.ai uses AI and deep learning technology to provide automated interpretation of radiology exams, such as X-rays, CT scans and ultrasounds, for medical imaging professionals, enabling faster diagnosis and treatment. Qure.ai offers a range of assistance, from personalized onboarding to continuous education and training. The venture capital-backed company also provides its technology to users via a mobile.
4. Medtronic
Medtronic designs, develops and deploys AI-enabled technologies designed to improve patient health. The company’s flagship AI-based product is the GI Genius intelligent endoscopy module, which uses AI to identify colorectal polyps during a colonoscopy. The system works by scanning every visual frame of the procedure in real-time and alerting physicians to the presence of lesions, including small, flat polyps that can go undetected by the human eye.
5. Teledoc Health
Teledoc Health is using AI through a partnership with Microsoft, integrating into the Teladoc Health Solo platform with Microsoft Azure OpenAI Service, Azure Cognitive Services and the conversational and ambient clinical documentation solution Nuance Dragon Ambient eXperience (DAX). The integrated solutions are designed to automate the creation of clinical documentation during virtual exams and improve the quality of shared medical information and care. Teledoc Health also developed an AI predictive model that forecasts the risk of uncontrolled diabetes, enabling more effective, timely patient interventions.
6. Pieces
Pieces uses generative AI to draft, chart and summarize clinical notes for doctors and nurses. To date, the company’s platform has generated more than 5.4 million patient summaries. The Pieces Working Summary provides an overview of the patient and is updated based on the latest electronic health record (EHR) documentation. The platform drafts daily progress notes to save time, reduce errors and improve billing. It also identifies and tracks discharge barriers for the patient to go home.
7. AITRICS
AITRIC’s AI-based clinical decision support system (CDSS) is designed to be integrated into hospital EHR systems to enable health care providers to monitor real-time changes in patient conditions. The FDA-approved VC patient monitoring dashboard is designed to help clinicians identify patients in critical condition. The software also provides detailed patient pages, allowing medical professionals to review comprehensive histories of vital signs and blood test results to provide proactive treatment.
8. SmarterDx
The SmarterDX platform uses AI to identify missed diagnoses in medical records to help health care providers catch potential errors or oversights, improve diagnostic accuracy and enhance patient safety. SmarterDx’s clinical AI is focused on capturing the correct diagnoses with evidence-based findings, which may include diagnosis-related group (DRG) downgrades. The algorithms provides automatic analysis, and findings aren’t sent to billing without validation by the health care provider.
9. Bayesian Health
Bayesian Health provides “intelligent care augmentation” through its AI-based Targeted Real-Time Early Warning System (TREWS) integrates within a hospital’s EMR system. The platform analyzes patient data, sending actionable clinical signals within existing workflows to help physicians and care team members catch life-threatening complications. The company’s AI is designed to think like a clinician by considering multiple data points in concert rather than individually.
10. AIRS Medical
AIRS Medical uses AI to enhance respiratory health diagnostics to help providers treat patients with respiratory issues and improve outcomes. The company develops AI-driven tools that analyze lung scans, such as CT scans, to help clinicians detect early signs of diseases, such as chronic obstructive pulmonary disease (COPD) and other lung conditions. The AI tools are also designed to integrate with any existing MRI machines without software and hardware upgrades.
About the Author
Phil Britt is a veteran journalist who has spent the last 40 years working with newspapers, magazines and websites covering marketing, business, technology, financial services and a variety of other topics. He has operated his own editorial services firm, S&P Enterprises, Inc., since the end of 1993. He is a 1978 graduate of Purdue University with a degree in Mass Communications.
Source: https://www.vktr.com/ai-market/10-top-ai-health-care-companies/
10 Top AI Health Care Products
What are some of the leading health care products in the AI market?
by Neil Savage
Products that incorporate artificial intelligence (AI) are making inroads into the health care industry, with major medical equipment manufacturers and software giants being joined by a range of startups. The Food and Drug Administration has granted approval to 691 medical devices based on AI and machine learning, as of last fall. Many of the devices draw on AI’s capabilities in image identification, enhancing the work of radiologists, while others are using large language models (LLMs) to provide assistance and suggest diagnostic insights, based on patient records and health care literature. Here, we examine some of the top products employing AI for health care applications.
1. IQ3
Butterfly Networks combines a handheld probe, a smartphone and AI software to create a portable ultrasound instrument that can be used even by technicians who have little experience with ultrasound. The IQ3 probe is built around semiconductor-based ultrasound-on-a-chip technology that captures high-resolution images and displays them on a smartphone. Its AI algorithms, hosted in the cloud on AWS, help identify issues of concern in the images. For instance, an algorithm trained on thousands of ultrasound lung images can automatically count B-lines — sonographic artifacts that can indicate breathing problems — based on a six-second clip. Counting them manually takes much longer.
2. Tempus One
The Tempus One virtual assistant gives clinicians quick access to a patient’s complete clinical and molecular profile. It also has access to an array of other data sets that the clinician can query to help them make clinical decisions. The software is a voice and text virtual assistant that uses generative AI built on large language models to answer questions and provide clinically supported guidance. It is designed to help clinicians deal with genomic testing that can inform decisions for personalized medicine. The assistant draws on Tempus’s health care dataset, which contains more than 100 petabytes of curated data.
3. Aidoc
Aidoc’s software for cardiovascular disease uses AI to examine medical scans and consolidate data to flag suspicious findings for human radiologists. The system provides triage to highlight high-priority cases and streamlines a radiologist’s workflow. It helps to coordinate recommended procedures for patients and can also alert clinical trial coordinators if a patient might be a good candidate for their trial. The software also manages patient follow-up for clinics and primary care physicians.
4. CT-3500
The CT-5300 scanner by Philips uses AI-based image reconstruction to allow imaging with an 80% lower radiation dose, 85% lower noise and a 60% improvement in low-contrast detectability. AI also drives a smart positioning camera, reducing the time it takes to position a patient by up to 23%, while improving manual centering accuracy by up to 50% and increasing consistency from user to user by up to 70%. A suite of AI-enhanced workflow tools help improve dose, speed and image quality in scanning for various conditions, from cardiac imaging to trauma.
5. Navina
The Navina generative AI assistant by the company Navina is designed to streamline the handling of large amounts of patient data, turning patient charts and information on separate computer screens into natural language interactions. The AI assistant helps doctors to understand a patient’s health status, handle administrative tasks, such as generating progress notes and referral documents, and get recommendations for care based on a patient’s data and up-to-date clinical guidelines.
6. Sonic DL
Sonic DL by GE Healthcare is a deep learning technology that allows magnetic resonance images to be captured up to 12 times faster than with conventional techniques. That’s fast enough to capture a high-quality cardiac image in the space of a single heartbeat, the company says. It reduces overall scan time by as much as 83% and doesn’t require patients to repeatedly hold their breath. Not only is holding breath tiring for patients who can do it, it prevents accurate scans for those who can’t. And prolonged scanning can also reduce image quality due to the increased chance of movement.
7. AI-Rad Companion
The AI-Rad Companion by Siemens Healthineers uses AI algorithms to automate the post-processing of imaging data sets. It aims to automate routine tasks in radiology to improve a radiologist’s workflow and handle high volumes of cases. It contains different modules for different modalities and regions of the body. For instance, for lung CT, it highlights nodules in the lungs and calculates volume, diameter and tumor burden. For brain MR, it automatically segments different brain structures and provides individual volumetric analysis.
8. Aquillon One/Genesis SP
The Aquillon One/Genesis SP by Siemens is a CT scanning platform that relies on deep learning to enhance imaging. It can image various aspects of the heart in the space of a single heartbeat as well as image multiple anatomical areas during one held breath and with one contrast injection. It uses adaptive iterative dose reduction to reduce radiation exposure while achieving resolution of 0.5 mm. The platform’s neural network was trained with high-quality imaging data of the whole body, including the brain, lungs, heart and musculoskeletal system.
9. Microsoft Fabric
Microsoft Fabric is a general-purpose analytics platform, and Microsoft Cloud for Healthcare has created tailored offerings within it to handle health care analytics and AI workloads. It allows organizations to bring together previously separate data, including electronic health records, picture archiving and communications systems, lab systems, claims systems and medical devices. With that, they can use text analytics for health, a Microsoft Azure AI language service that allows them to get patient care insights from unstructured data in seven languages. The platform also allows them to use Azure’s AI Health Bot to customize an assistant for managing clinical and administrative workloads and AI Health Insights to assist doctors in decision making.
10. Curie
Curie by Enlitic is an AI-powered data management framework for medical images. It contains applications that allow for data standardization, including consistent and clinically relevant labeling. It allows images to be analyzed in real-time as well as historically and creates an imaging database to enhance research. Modules offer data anonymization and de-identification. The framework speeds up workflow and reduces repetitive tasks and can also reduce billing errors due to consistent labeling, the company says.
About the Author
Neil Savage is a freelance science and technology writer. His focus areas include photonics, physics, computing, materials science and semiconductors. He has written for both the popular press and trade publications and websites, including Discover, IEEE Spectrum, Technology Review, New Scientist, Nature Photonics, OE Magazine, the Boston Globe and Xconomy. He is a 1997 graduate of Boston University’s College of Communications with an M.S. in science journalism and has a B.A. in English from the University of Rochester.
Source: https://www.vktr.com/ai-platforms/10-top-ai-health-care-products/
5 AI Case Studies in Health Care
by Pamela Cagle
How are health care providers using AI technologies to solve the challenges they’re facing?
Health care organizations are using artificial intelligence (AI) to overcome many of their data-based challenges. They’re demonstrating the practical applications of AI in reshaping patient care, medical training and operational efficiency. See how some health care providers are getting results with AI in these case studies:
1. TidalHealth Peninsula Regional
Although TidalHealth Peninsula Regional, a Level II trauma center, invested in a variety of drug information databases and other clinical decision-support solutions, it found that clinicians were still spending far too long searching for answers, according to a case study.
Their challenges included the following: clinicians spending too much time searching for relevant patient data and clinical evidence in different systems; a lack of easy access to best practices and evidence-based guidelines leading to variations in care delivery; and delays in finding crucial information impacted patient outcomes.
“Traditional search methods in drug information tools are a bit clunky,” says Rachel Cordrey, Pharm.D., supervisor of inpatient pharmacy operations, TidalHealth Peninsula Regional. “You have to open a web browser or open a desktop shortcut, search by the medication name you are looking for and then search through the results to find the information you need to make a clinical decision.”
TidalHealth worked with IBM to implement IBM Micromedex with Watson AI, a cloud-based clinical decision support (CDS) system. The system combined several capabilities for TidalHealth: it aggregated clinical information from various sources, including drugs, diagnose, and therapeutic procedures; used natural language processing (NLP) and machine learning (ML) to understand user queries and provide relevant, evidence-based recommendations; and integrated with TidalHealth’s electronic health record (EHR) system.
Results
- Clinicians reported saving up to 20 minutes per encounter
- Increased adherence to best practices
- Increased consistency in care
2. Portal Telemedicina
The rural health care provider Portal Telemedicina faced challenges in its health care delivery systems, such as the fragmented care, inefficient systems and siloed health care systems, according to a case study.
“Taming this fragmented ecosystem of devices and data was crucial to large-scale adoption of our solution,” says Rafael Figueroa, CEO, Portal Telemedicina.
Portal Telemedicina worked with Google Cloud on the health provider’s data aggregation, storage and analysis. Cloud SDK routes data through a gateway to cloud storage. AI classifies medical findings and recommends treatment urgency, such as analyzing chest X-rays for pneumonia.
Results
- 20% reduction in hospital admissions
- 5% reduction in health care costs
- Improved patient outcomes
3. Amsterdam UMC
Amsterdam University Medical Centers (UMC) ran into some issues in improving patient care and research as well as operational efficiency, according to a case study.
Amsterdam UMC needed to manage and analyze the vast amounts of data generated from various sources, including patient records, clinical trials and research studies. It encountered difficulty using the collected data for research purposes and improving patient care. The organization also required advanced analytics to predict patient outcomes and optimize treatment plans
“Our opportunity is to use AI to help us with our ever-growing data volumes,” says Dr. Geert Kazemier, professor of surgery and director of surgical oncology, Amsterdam UMC.
The team at Amerstam worked with SAS and its AI-based analytics platform: they integrated data from diverse sources for a unified environment; provided advanced generative AI analytics tools for research and predictive modeling; and clinicians and researchers have real-time access to critical data.
Results
- Improved patient care through personalized treatments based on data insights
- Innovative research capabilities through advanced analytics
- Streamlined data management
- More efficient research processes
4. Megi Health Platform
Megi Health was created as a spinoff of the Magdalena Clinic to supplement their staff by automating tasks, such as gathering medical histories and patient reminders.
Megi Health needed to ensure its platform was secure for medical information and easy to use for patients, according to a case study.
Megi Health worked with Infobip and its Answers solution for Megi Health’s interactive WhatsApp-based chatbot that provides 24/7 access to medical information, symptom checks and basic consultations. The chatbot tailors responses based on individual patient needs and medical history, offering personalized health tips and reminders. Patients can also connect with health care providers for consultation and appointments.
“Learning best practices in chatbot design and customer experience … has accelerated the project and helped us create a simple chatbot that provides real value to our patients,” says Nina Šesto, CEO, Megi Health. “Using the self-service platform, we get full access to all of the answers’ features, and we are excited about making further enhancements to make Megi even more helpful.”
Results
- An average CSAT score of 86%
- A 65% reduction in data collection time because streamlined information gathering
- Improved medication adherence
5. GE Healthcare
GE Healthcare needed to improve the training and maintenance of complex medical equipment for technicians working remotely. Traditional methods were expensive, time-consuming and risked damage to delicate components, according to a case study.
“The Voyager MRI has an intricately wired system cabinet, analogous to a stack of servers in an IT center,” says Hui Gao, head of technical operation, GE Healthcare. “Even in a hands-on environment, it’s difficult to understand the connections and how different pieces link together. Sometimes, the theory is not easy for students to understand. And the signal flow — how the signal goes from this port to another port to this circuit board to another board — is difficult to show.”
GE Healthcare worked with Microsoft to develop a mixed reality (MR) training and maintenance program using Azure IoT and HoloLens 2, allowing technicians to wear a headset displaying interactive 3D holograms overlaid on the equipment. The holograms provide step-by-step instructions, troubleshooting guides and relevant data visualizations.
Audible AI-based narration through Azure AI and Cognitive Services helps the GE trainee follow step-by-step training.
Results
- Reduced training time by 50%
- Improved first-time fix rate by 30%
- Lowered maintenance costsIncreased technician satisfaction
About the Author
Pamela Cagle is a health and technology writer. With a strong foundation in nursing as a former R.N. and years of experience in digital marketing, Cagle blends medical expertise with storytelling to develop content. Her lens lends authenticity to health and wellness topics. She’s written for AARP, the Council on Aging and various companies. She holds a bachelor’s in healthcare administration from Colorado Technical University.
Fuente: https://www.vktr.com/ai-disruption/5-ai-case-studies-in-health-care/