A recent bipartisan initiative by Senators Martin Heinrich, Mike Rounds, Marsha Blackburn and Todd Young urged the Centers for Medicare & Medicaid Services (CMS) to establish a formal payment pathway for algorithm-based health care services (ABHS). This move addresses the need for stable Medicare reimbursement for FDA-cleared AI and machine learning (ML) medical devices, which aid in diagnosis and treatment.

This initiative underscores AI’s transformative potential in healthcare, prompting Deepak Sahu, Managing Director at Trinity Life Sciences, and Michelle  Wu, Co-founder & CEO at NyquistAI, to analyze the current landscape of approved AI medical devices and their global pipeline powered by the NyquistAI platform. Our review covers the U.S., China, Australia and the EU4, highlighting the innovations and strategies of leading companies. Currently, over 414 companies have at least one approved AI device, with nine companies, including Siemens, GE, Canon, Aidoc Medical, Shanghai United Imaging, Philips, RapidAI, Samsung and viz.ai, Inc., having more than 10 approvals. Our insights aim to illuminate the dynamic AI landscape and its impact on advancing patient care.

The pipeline of AI medical devices is robust, and as these technologies evolve, they will undoubtedly play a transformative role in shaping the future of medical care worldwide. We are currently tracking more than 750 clinical trials of AI medical devices globally spread across various therapeutic areas. The top areas by the number of trials are Otolaryngology (ENT) with 90 trials, Neurology with 89 trials, Oncology with 79 trials, Radiology with 70 trials, and Cardiology with 41 trials, with more than 650 being interventional trials.

Clinical Applications of AI

Based on the recent trials analyzed and the products in the pipeline, here are the key application areas of AI in healthcare:

Otolaryngology (ENT) (11% of the pipeline)

AI applications in otolaryngology (ENT) are transforming the diagnosis, treatment and management of ear, nose and throat disorders. These applications include:

• Voice and Speech Analysis: Analyzing voice and speech patterns to diagnose conditions such as vocal cord disorders, speech impairments and other ENT-related conditions
• Hearing Loss Detection: Detecting and assessing hearing loss with AI-driven audiometric tools, enabling early intervention and customized treatment plans
• Sinus and Nasal Disorder Diagnosis: Diagnosing sinusitis and other nasal disorders through advanced imaging and symptom analysis
• Sleep Apnea Detection: Analyzing sleep patterns to diagnose and manage sleep apnea, improving patient outcomes
• Surgical Planning and Navigation: Planning and executing complex ENT surgeries, enhancing precision and reducing risks

Neurology (11% of pipeline)

AI supports the management of neurological disorders by predicting disease courses and assisting in treatment planning.

• Stroke Prediction and Diagnosis: Evaluating risk and assisting in the rapid diagnosis of stroke
• Seizure Detection: Monitoring and predicting seizure activity in epilepsy patients using AI algorithms
• Parkinson’s Disease Management: Analyzing movement data to track the progression and tailor treatments for Parkinson’s disease

Oncology (10% of pipeline)

AI is transforming oncology by enhancing early cancer detection, improving diagnostic accuracy and personalizing treatment approaches.

• Cancer Detection and Diagnosis: Analyzing patterns in medical images and genetic data for early diagnosis
• Treatment Planning: Utilizing detailed patient data to develop tailored treatment strategies
• Predicting Treatment Response: Predicting how individuals will respond to various treatments, helping to customize care plans
• Drug Discovery: Accelerating the discovery of new oncological drugs through AI-driven simulations and data analysis

Radiology (9% of pipeline)

Radiology represents one of the earliest adopters of AI technologies, where they have significantly improved the interpretation of imaging results. AI applications in radiology include:

• Image Interpretation: Enhancing the clarity and detail of images to aid in more accurate diagnoses
• Disease Detection and Diagnosis: Utilizing AI to identify subtle patterns that may be indicative of early disease stages
• Predicting Disease Progression: Predicting how diseases like cancer may progress, influencing treatment decisions
• Treatment Planning: Using detailed imaging data to plan interventions, surgeries or radiation therapy more effectively

Cardiology (5% of pipeline)

In cardiology, AI tools analyze heart rate patterns, detect irregularities and even assist in the management of chronic conditions such as heart failure.

• Heart Disease Diagnosis: Assessing heart imaging studies and patient data to diagnose conditions more swiftly
• Risk Prediction: Evaluating patient data to forecast the likelihood of heart disease, guiding preventive measures
• Treatment Planning: Personalizing treatment plans based on individual patient profiles, improving outcomes
• Monitoring Patient Health: Alerting healthcare providers to changes in a patient’s condition through the use of continuous monitoring systems

Hematology (4% of pipeline)

AI enhances the understanding and treatment of blood disorders.

• Genome Sequencing and Gene Expression: Assisting in detailed genetic analyses to understand hematological disorders
• Modeling Gene Networks: Providing insights into gene interactions and their implications for disease
• Analysis and Clustering of Gene Expression Data: Identifying patterns that help predict disease behavior and treatment responses

Surgery (4% of pipeline)

AI is increasingly integrated into surgical procedures, improving outcomes and efficiency.

• Preoperative Planning: Analyzing medical records to optimize surgical planning
• Intraoperative Guidance: Providing real-time AI assistance during surgeries to support decision-making
• Integration into Surgical Robots: Enhancing robotic surgery with advanced AI capabilities for precision
• Postoperative Care: Monitoring and predicting post-surgical outcomes to improve patient recovery

Pathology (3% of Pipeline)

In pathology, AI significantly enhances the analysis of tissue samples, improving both speed and accuracy.

• Cancer Detection: Detecting cancerous cells in biopsy samples using AI tools
• Disease Progression: Monitoring disease progression through automated analysis of patient data over time
• Personalized Treatment: Utilizing genetic data to tailor therapeutic approaches for individual patients

Endocrinology (3% of pipeline)

AI assists in managing diseases like diabetes by predicting and monitoring disease indicators.

• Diabetes Management: Predicting blood sugar levels and recommending insulin dosages
• Thyroid Disorder Diagnosis: Diagnosing thyroid disorders from patient data using AI algorithms
• Hormone Level Analysis: Analyzing hormone levels to manage and diagnose endocrine disorders

Pulmonology (2% of pipeline)

AI is used in pulmonology to diagnose lung conditions and optimize treatment plans.

• Asthma Management: Predicting asthma attacks and adjusting treatment plans accordingly
• COPD Diagnosis and Management: Diagnosing and monitoring Chronic Obstructive Pulmonary Disease through AI-enhanced imaging analysis

Ophthalmology (2% of pipeline)

AI technologies are crucial in detecting and managing eye diseases, potentially preventing blindness.

• Diabetic Retinopathy Screening: Screening for early signs of diabetic retinopathy in retinal images
• Age-Related Macular Degeneration and Glaucoma Detection: Detecting early signs of major eye diseases using detailed imaging analyses
• Cataract Surgery: Assisting in surgery planning and execution, including lens measurements
• Vision Tests: Automating and enhancing the accuracy of vision tests

As these trials progress and more AI medical devices receive approval, the future of healthcare looks poised for significant advancements. These innovations promise to improve diagnostic accuracy, personalize treatment plans and ultimately enhance patient outcomes across various medical fields.

Authors: Deepak Sahu, Managing Director at Trinity Life Sciences, and Michelle (武丹 Dan) Wu, Co-founder & CEO at NyquistAI