Hazadvisors.com is proud to be one of the leading providers of AI technology in health care. AI technology is revolutionizing the industry, transforming how medical professionals approach diagnosing diseases, managing patients, and optimizing hospital day-to-day operations.
While many companies have helped to propel the boom artificial intelligence (AI) has enjoyed in the past decade, the most effective solutions are often proprietary systems tailored to an organization’s unique needs.
Exploring the leading providers of AI technology in healthcare
Let’s review the current artificial intelligence landscape in healthcare today and discuss why building your AI solution might be optimal:
The increasing use of artificial intelligence in healthcare
AI is already performing various roles in healthcare today, from early disease detection to reducing administrative burdens on medical professionals. For example, AI is used to predict the risk of readmission for patients who have had heart failure, flag abnormalities in imaging scans, and track disease progression.
Healthcare providers like the Mayo Clinic have adopted machine learning systems to improve diagnostic accuracy, boost patient experience, and reduce mortality rates.
While many vendors market off-the-shelf AI solutions, these systems frequently come with substantial limitations, like rigid functionalities that make it impossible to adapt them to an organization’s unique workflows.
On the other hand, propriety artificial intelligence solutions are customized for an organization’s unique needs, providing increased flexibility that makes it easier to align with an institution’s internal goals.
Evaluating the leading providers of AI technology in healthcare
Here’s a quick look at some of the key players providing AI solutions to companies in the healthcare space:
1. IBM Watson Health
IBM Watson provides AI solutions that support the diagnosis and treatment of cancer patients. The strength of this algorithm lies in its ability to sort through vast datasets and recommend personalized care plans for patients.
IBM offers tools like Watson for Oncology, which is used to analyze patient data and recommend treatment options.
IBM focuses on using natural language processing (NLP) to analyze structured and unstructured healthcare data, reducing the time it takes healthcare providers to derive actionable insights.
Key offerings
- Watson for Oncology: A powerful AI solution that assists oncologists with patient care recommendations, imaging analyses, and population health management.
- Watson Health Imaging: Provides AI-driven tools that help to identify anomalies in medical imaging like X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI).
- IBM Clinical Development: This platform accelerates the design and implementation of clinical trials with predictive analytics.
- Population Health Insights: Offers predictive artificial intelligence models that help manage chronic diseases. These solutions also help to optimize care delivery for large patient populations.
Limitations
- Limited customizability: IBM Watson Health is built for general applications and isn’t fully adaptable to the unique workflows of every healthcare organization.
- High implementation costs: IBM Watson Health’s solutions require significant financial investment.
- Mixed accuracy reports: Some studies have questioned Watson’s reliability in diagnosing complex medical conditions.
- Data Silos: Integrating Watson’s AI solutions with existing infrastructure can be challenging, especially for healthcare organizations with fragmented IT environments.
2. Google Health
Google Health’s DeepMind technology has excelled in analyzing medical imaging tools like magnetic resonance imaging, computed tomography scans, and X-rays. It can detect diseases such as diabetes and cancer as accurately as human experts. The algorithm has sometimes proven more accurate at making diagnoses than physicians.
DeepMind has emerged as a significant player in medical diagnostics and imaging. For example, one of its algorithms can detect over 50 eye diseases from retinal scans. Google Health collaborates with major healthcare organizations to integrate AI with electronic health records (EHR) and patient care pathways.
Key offerings
- DeepMind Health: Creates AI models for predicting patients’ risks of developing diseases such as acute kidney injuries, sickle cell amenia, and retinal diseases.
- Medical Imaging: Google’s AI has proven particularly effective at analyzing medical images. It can accurately detect cancer and other conditions. Some of these solutions can be more effective than human eyes at detecting anomalies in medical scans.
- Care Studio: This intuitive interface collects patient data and uses it to streamline medical records to help healthcare professionals with decision-making.
- TensorFlow for Healthcare: Offers open-source tools that healthcare organizations can use to create custom AI solutions for patient monitoring, drug discovery, and disease diagnosis.
Limitations
- Data privacy concerns: Google’s platforms have sparked debates over compliance with the Health Insurance Portability and Accountability Act (HIPPA) and other regulations when handling medical data.
- Narrow focus: While Google’s artificial intelligence solutions have been exceptional at analyzing medical images, they lack robust offerings for operational challenges that can be automated.
- Reliance on Google Ecosystem: Healthcare providers might find themselves tied to Google’s ecosystem, limiting flexibility.
3. Microsoft Azure Health AI
Microsoft offers tools that help medical organizations to adopt AI solutions. Its algorithms prioritize using natural language processing to create a more efficient approach to patient record management, risk stratification, and predictive analytics.
As advanced as these tools are, they usually require significant customization to meet an organization’s specific needs.
Developed under the Microsoft Cloud for Healthcare initiatives, Microsoft’s machine learning algorithms for use in healthcare are designed to improve operational efficiency and enhance care delivery. These systems leverage Azure’s cloud services to provide scalable AI solutions that perform various tasks, from predicting patient deterioration to personalizing correspondences with patients.
Key offerings
- Azure Health Bot: A conversational AI that assists with triaging, symptom checking, and delivering health-related information to patients.
- Microsoft Genomics: Stores and analyzes vast genomic datasets for precision medicine and genetic research.
- Healthcare-specific machine learning models: Azure offers pre-built artificial intelligence solutions that perform various tasks like predicting patient outcomes, detecting fraud, and optimizing resource allocation.
- AI-powered telemedicine tools: Azure’s integration with Microsoft Teams allows for secure telehealth consultations.
Limitations
- Customization limitations: The dependency on Azure ecosystems might constrain healthcare organizations that use different infrastructures. Microsoft’s solutions often require significant customization to align with an organization’s needs.
- Dependence on cloud infrastructure: Like other cloud-based AI platforms, Microsoft’s artificial intelligence models require robust internet connectivity that is compliant with data-sharing regulations.
- Competition with existing IT systems: Integrating Azure with legacy healthcare systems can be cumbersome.
4. Philips Healthcare
Philips Healthcare focuses on integrating artificial intelligence into diagnostic and imaging equipment. Its IntelliSpace AI Workflow Suite allows healthcare providers to automate complex tasks like anomaly detection, emergency response, and image reconstruction.
Key offerings
- AI-enhanced ultrasound: Philip’s AI-powered ultrasound machines improve image quality considerably, increasing clinicians’ confidence in their diagnoses.
- IntelliSpace AI Workflow Suite: Integrates AI models into diagnostic processes, streamlining physicians’ workflow.
- Patient monitoring systems: Helps to prevent and predict patient deterioration in critical care units.
- AI in Sleep and Respiratory care: Provides AI tools for monitoring and managing health conditions like sleep apnea.
Limitations
- Integration hurdles: Philips AI tools are typically tied to the company’s hardware system, which limits interoperability.
- Limited breadth: Philip’s AI capabilities are concentrated mainly on imaging, anomaly detection, and patient monitoring.
- Dependency on proprietary devices: Philips AI systems frequently require specific equipment, which puts an additional financial burden on healthcare providers.
5. GE Healthcare AI
GE Healthcare AI combines artificial intelligence artificial intelligence with its expertise in hardware to deliver models that aid medical imaging and diagnostics. Their solutions are widely used to improve radiologists’ workflows by increasing the accuracy and speed of diagnostic tests.
Key offerings
- Revolution Apex CT Scanner with AI: This tool enhances the quality of medical images, reducing the need for repeat scans.
- Edison AI Platform: This cloud-based platform gives radiologists access to AI tools for diagnostics, flagging critical cases, and image processing.
- AI for Predictive Maintenance: Predicts when medical equipment might fail, reducing downtime.
- Digital Twins Technology: Creates virtual reality models of healthcare systems and devices to improve patient outcomes, aid decision-making, and optimize workflows.
Limitations
- Niche focus: GE Healthcare AI is highly specialized in diagnostics and imaging, offering limited solutions for broader challenges in healthcare.
- Costly hardware dependencies: Many of GE’s AI tools are only compatible with their proprietary imaging equipment, increasing setup costs.
- Limited integration: Hospitals using non-GE hardware will need help integrating GE’s artificial intelligence tools into their systems.
6. Amazon AI for Healthcare
Amazon has developed a suite of artificial intelligence under Amazon Web Services. Its offerings are helping to transform the healthcare ecosystem by leveraging machine learning algorithms to improve diagnostic accuracy and optimize operations.
AWS provides healthcare organizations with tools to build and integrate in-house AI solutions into existing systems. The company also offers pre-built artificial intelligence.
Key offerings
- Amazon Comprehend Medical: This NLP solution can extract medical solutions from unstructured text, like a physician’s note or clinical trial reports. It streamlines how patient data is analyzed.
- Amazon Transcribe Medical: A speech-to-text service designed specifically for healthcare professionals, providing accurate transcriptions of consultations and clinical dictations.
- Amazon HealthLake: This service allows healthcare professionals to store, transform, and query unstructured and structured healthcare data using the Fast Healthcare Interoperability Resources (FHIR) standard.
- AI for Predictive Analytics: AWS-powered machine learning models that predict patient outcomes, optimize hospital workflows, and manage population health.
Limitations
- Data privacy concerns: Using cloud-based AI solutions raises concerns about data security, especially concerning sensitive patient data.
- Limited customizability: While AI provides the building blocks for artificial intelligence solutions, customizing them for an organization’s unique needs requires significant in-house expertise and resources.
- Steep learning curve: Organizations that employ limited technical staff might find the setup and service requirements of AWS’s AI solutions overwhelming.
6. Nvidia AI for Healthcare
Nvidia is one of the top developers of the software and hardware platforms that power AI applications in healthcare. It focuses on improving the computational power required to run deep-learning algorithms. Nvidia provides tools that allow for faster, more accurate diagnostics.
Key offering
- Nvidia Clara: A Platform that provides developers with pre-trained AI models for tasks like tumor detection, organ segmentation, and DNA sequence analysis.
- GPU-powered accelerators: Nvidia provides high-performance graphics processing units (GPUs) that enable the training and deployment of complex machine learning models used in the healthcare industry.
- Federated Learning Frameworks: Allows collaboration between multiple healthcare institutions to train AI models without directly sharing sensitive data.
- AI for Drug Discovery: Nvidia collaborates with pharmaceutical companies to accelerate drug discovery by simulating molecular interactions using AI.
Limitations
- High costs: Nvidia’s cutting-edge GPUs and AI solutions can be expensive for smaller healthcare providers and startups.
- Complexity: Specialized technical knowledge is required to leverage Nvidia’s AI tool.
- Limited focus: Nvidia primarily focuses on providing genomics and imaging, which may not address the broader needs of healthcare organizations, such as automating operational workflows.
7. Oracle AI in Healthcare
Oracle is a global leader in providing enterprise solutions, cloud infrastructure, and AI solutions for the medical industry. Its tools aim to optimize operations and improve the quality of care offered to patients.
Key offerings
- Oracle Health Management Cloud: An AI system to predict patient health outcomes. It also helps to streamline patient care, particularly for those with chronic conditions.
- AI-Powered Supply Chain Management: Helps with medical supply inventories by predicting demand and potential disruptions.
- Integrated EHR Solutions: Oracle’s acquisition of Cerner has raised its ability to integrate artificial intelligence solutions into electronic health records for improved patient insights and analytics.
- Oracle Digital Assistant: Helps organizations with patient management through AI-powered chatbots to perform tasks like appointment scheduling, symptom checking, and responding to frequently asked questions.
Limitations
- Complex implementation: Oracle provides enterprise-level solutions that require significant expertise and resources to set up.
- Limited focus on clinical AI: Most of Oracle’s AI solutions are focused on improving operational efficiency rather than patient care. Its diagnostic and imaging offerings need to be expanded.
- Dependence on Oracle’s ecosystem: Full utilization of Oracle’s artificial intelligence tools requires total commitment to its broader suite of tools, limiting flexibility.
8. Epic Systems AI
Epic Systems is a leader in healthcare IT, and it has incorporated AI into its widely used EHR platforms. Epic’s primary focus has been on using AI to improve data analytics and care delivery.
Key offerings
- Cognitive Computing in EHR: Epic leverages artificial intelligence to provide insights from EHR data, helping clinicians to identify trends and predict patient outcomes.
- Sepsis Model: A powerful AI tool that alerts clinicians to the early signs of sepsis, helping to save lives.
- Epic Cosmos: This data aggregation platform uses AI to analyze the health data of populations based on millions of patient records.
- MyChart AI Features: This tool enhances patient engagement by integrating predictive analytics with Epic’s personalized patient portal.
Limitations
- System-specific features: Epics artificial intelligence solutions are primarily designed for use within its EHR ecosystem, limiting adaptability for organizations.
- High cost of entry: Setting up Epic’s AI-enhanced system might be outside the budgets of smaller healthcare providers.
- Interoperability: Integrating Epic’s AI systems with external software can be challenging, as is often the case with EHR systems.
9. Siemens Healthineers
Siemens has emerged as one of the leading providers of AI technology in healthcare, helping with workflow automation, clinical diagnostics, and operational efficiency. Its AI-driven solutions address challenges in imaging, diagnostics, and therapy.
Key offerings
- AI-Rad Companion: This tool assists physicians by automatically analyzing medical images and generating detailed reports. It supports imaging modalities such as CT scans and MRIs.
- AI Pathway Companion: Supports clinical decision-making by analyzing patient data and providing treatment pathway recommendations tailored to individual patients.
- Digital Twin Technology: Siemens uses AI to create digital twins of patients, allowing for precise simulations for personalized treatments.
- Automation in Lab diagnostics: AI algorithms that help optimize lab workflows and increase the accuracy of diagnostic tests.
Limitations
- Hardware dependency: Siemens AI tools often require using their proprietary hardware, increasing setup costs.
- Scalability challenges: Smaller healthcare systems may need help implementing Siemens’ high-tech solutions due to high resource requirements.
- Complexity of use: The advanced features of Sieman’s AI solutions require extensive training to maximize their utility.
10. Viz.ai
Viz.ai’s healthcare solutions are known for their focus on streamlining critical care workflows. The company uses artificial intelligence to help with time-sensitive medical conditions like strokes. Viz.ai’s systems have been approved by the Food and Drug Administration (FDA) to improve communication among healthcare teams and clinical decision-making.
Viz.ai’s algorithms for healthcare are a testament to the transformative potential of artificial intelligence in reducing diagnostic errors, shortening treatment times, and improving the outcomes of patients with life-threatening conditions. The company plans to expand into other critical care areas in the coming years.
Key offerings
- AI-Powered Stroke Detections: Viz.ai uses machine learning algorithms to identify large vessel occlusions (LVOs) on CT angiograms in minutes, alerting care teams for faster interventions.
- Viz Neuro Suite: A collection of tools to diagnose and treat neurological conditions, including strokes, aneurysms, and intracranial hemorrhages.
- Care Coordination platform: Facilitates real-time communication between emergency services and physicians, ensuring patients promptly reach facilities that offer the best care possible.
- Scalable Integration: Viz.ai’s tools integrate seamlessly with hospital information technology systems, including EHRs and premature atrial contractions (PACS), without overhauls.
Limitations
- Specialized focus: While excellent for acute conditions like strokes, Viz.ai’s tools have limited applications outside neurology and cardiology.
- Reliance on connectivity: Viz.ai’s platforms depend on robust network infrastructure to enable real-time alerts and coordinate care.
- Subscription-based pricing: The subscription costs of Viz.ai’s tools can be an entry barrier for smaller healthcare facilities and those with limited budgets.
Why propriety AI is superior
Investing in AI solutions that are built in-house with input from internal stakeholders often outperforms the most advanced pre-built solutions. Some of the reasons why propriety algorithms are generally the best alternative include:
- Tailored to your needs: Proprietary artificial intelligence solutions can be adapted to your specific workflows to meet your organization’s ever-evolving needs.
- Continuous feedback loops: Developing AI solutions in-house ensures constant refinement as stakeholders can provide direct input to increase usability.
- Enhanced data security: Keeping sensitive patient data within your organization’s ecosystem minimizes risk exposure.
- More cost-effective over time: While upfront investments might be higher with proprietary solutions, these systems typically offer improved efficiency and reduced long-term operational costs. For example, AI algorithms that integrate seamlessly with existing electronic health records can predict patient outcomes more effectively than standalone systems.
Building proprietary artificial intelligence solutions
Some of the steps you should take if you’re considering adopting AI for your practice include:
- Get training: Take courses to familiarize yourself and your team with ways to use artificial intelligence to streamline healthcare processes. This ensures you have the skills to develop and manage AI solutions that align with your organization’s needs.
- Engage stakeholders early: Collaborate with other healthcare providers as you develop AI solutions that address real-world challenges for your practice.
- Prioritize data governance: Establish protocols to secure patient data early to prevent unethical uses.
- Iterate continuously: Proprietary AI solutions should be living projects that are regularly updated based on performance metrics and user feedback.
Training programs on artificial intelligence governance and forward-looking courses on data science topics for the next ten years provide a solid foundation for organizations looking to develop AI solutions.
The future of AI in healthcare
The focus as AI’s role in the medical industry continues to expand rapidly is creating patient-centric, intelligent, intuitive, and ethical systems. The most impactful solutions emerging combine cutting-edge technology with deep organizational knowledge, making proprietary AI the ideal choice for healthcare providers.
Consider leveraging professional training resources to gain expertise within your organization so you can transform how you approach patient care and administrative duties with AI.