AI Models, LLMs - Relevance in the Healthcare Segment

Introduction

This article extends my previous piece on AI topologies in healthcare by profiling the AI model landscape, leading solution providers, and the evolving role of hyperscalers. We will now explore the models to companies to providers to market.

As AI accelerates across the healthcare value chain—from administrative efficiency, imaging and drug discovery, to continual monitoring, to clinical decision, and treatment options support—understanding the models, key players, and emerging business models becomes critical.

Image from Adobe Stock (MrPanya)

The key projections are very promising thus far:

• Market to grow from $22.45B (2023) to over $208B by 2030

• CAGR ~37% expected over the next 5-10 years

• Software segment to dominate; Asia-Pacific to lead future growth

AI Models Powering Healthcare

AI in healthcare leverages various models, broadly falling under:

• Machine Learning (ML) models:

  • Supervised Learning: Used for tasks like image classification (e.g., detecting tumors, broken bones, unnatural growth etc.. in scans), disease diagnosis based on patient data, and predicting patient outcomes.

    
    
    
    

  • Unsupervised Learning: Applied for identifying patterns in large datasets, such as patient subgroups for personalized treatments or drug repurposing.

    
    
    
    

  • Reinforcement Learning: Shows promise in optimizing treatment plans, robotic surgery, and drug discovery by learning from trials and errors.

    
    
    
    

• Deep Learning (DL) models: 

  A subset of ML, particularly effective with complex, high-dimensional data like medical images, genomics, and electronic health records (EHRs).

  
  
  
  

  • Convolutional Neural Networks (CNNs): Dominant in medical image analysis (X-rays, MRIs, CT scans) for detecting anomalies, and diagnoses support.

    
    
    
    

  • Recurrent Neural Networks (RNNs) / Transformers: Used for processing sequential data like EHRs, patient notes, and genomic sequences, enabling tasks like clinical note summarization, predicting disease progression, and personalized medicine.

    
    
    
    

• Generative AI models (including LLMs):

  • Generative Adversarial Networks (GANs): Used for synthetic data generation (for augmenting limited datasets), image reconstruction, protein synthesis, analysing adverse effects, and drug discovery.

    
    
    
    

  • Large Language Models (LLMs): Increasingly used for natural language processing tasks in healthcare, such as patient communication and administrative tasks.

    
    
    
    

LLMs for Health Segment

A failry detailed overview on this topic is covered as a part of my earlier article https://www.semiconductorconsulting.de/post/llms-in-healthcare

Leading Companies and Healthcare AI focus

Major companies are already invested in AI based healthcare -

• Google (Google Cloud, Google AI, DeepMind):

  
  

  • MedLM: A suite of AI models specifically designed for the medical domain, available through Google Cloud's Vertex AI. It helps answer medical questions, summarize dense information, and derive insights from unstructured data.

    
    

  • Open Health Stack: Open-source building blocks for healthcare app development.

    
    
    
    

  • Google Lens: Can help visually search for skin conditions.

    
    

• Microsoft (Azure AI, Microsoft Healthcare Bot, Nuance Communications):

  
  

  • Strong focus on leveraging Azure's cloud infrastructure for healthcare data and AI workloads.

    
    

  • Microsoft Healthcare Bot: A prominent AI conversational agent for patient engagement.

    
    

  • Nuance Communications: Acquired by Microsoft, Nuance's Dragon Medical One is widely used for clinical documentation, and its AI capabilities are being integrated into Microsoft's healthcare offerings.

    
    

• Amazon (AWS, Amazon HealthLake, Amazon Comprehend Medical):

  
  

  • AWS for Health: Provides cloud services and solutions tailored for healthcare and life sciences, enabling data storage, analytics, and AI/ML model deployment.

    
    

  • Amazon HealthLake: A service for healthcare providers, pharmaceutical companies, and insurers to store, transform, query, and analyze health data.

    
    

  • Amazon Comprehend Medical: A natural language processing (NLP) service that uses ML to extract relevant medical information from unstructured text.

    
    
    
    

• NVIDIA (NVIDIA Clara, MONAI):

  
  

  • NVIDIA Clara: A comprehensive platform that accelerates medical imaging workflows using generative AI.

    
    
    
    

  • MONAI (Medical Open Network for AI): An open-source framework for AI-assisted medical imaging applications, widely used for research and development.

In addition, many smaller comapnies are developing specialized LLMs or integrating general LLMs into their healthcare specific platforms with a better focus on local regulations, nuances and data prvacy and compliance.

Market Demand: Customer base...

The typical customer base for AI based healthcare segment include -

• Hospitals and Health Systems

• Pharmaceutical and Biotech Companies

• Medical Device Manufacturers

• Insurance Companies

• Diagnostic Laboratories

• Patients

• Government Agencies and Public Health Organisations

• Research Institutions

  
  

  AI-Based Health Firms and Their Use Cases

  
  

  Some of the companies utilizing AI in healthcare segments

  
  

• Diagnostics & Imaging:

  • Aidoc: AI for radiologists to flag critical issues in CT scans (e.g., strokes, pulmonary embolisms), accelerating diagnosis.

    
    

  • Butterfly Network: Handheld, AI-powered ultrasound device for point-of-care imaging and diagnosis.

    
    

  • Caption Health: AI to guide clinicians in capturing and interpreting ultrasound images.

    
    

  • Cleerly: AI-driven cardiovascular disease detection by analyzing coronary CT angiography.

    
    

  • Enlitic: Deep learning for improved diagnostic accuracy in medical imaging, assisting radiologists.

    
    
    
    

• Drug Discovery & Development:

  • BenevolentAI: Uses AI to identify new drug targets and accelerate drug discovery.

    
    

  • Exscientia: AI-driven drug design and development, aiming to reduce the time and cost of bringing new drugs to market.

    
    

  • Owkin: Applies machine learning to biomedical research, accelerating drug discovery and development, particularly in precision medicine.

    
    

  • Insilico Medicine: Uses generative AI for drug discovery and aging research.

    
    
    
    

• Personalized Medicine & Treatment:

  • Aitia: Uses machine learning to match patients with the most effective treatments.

    
    

  • Oncora Medical: AI-driven oncology solutions for personalized cancer treatment plans

    
    

• Patient Engagement & Care Management:

  • Voiceoc: Conversational AI for automating patient communication, appointment scheduling, and patient engagement.

    
    

  • Sully.ai (Parikh Health): AI-driven check-in systems and automation of front desk tasks to enhance operations and patient care.

    
    

  • Microsoft Healthcare Bot: AI-powered conversational agent for patient engagement, appointment scheduling, and virtual triage.

    
    

• Administrative & Operational Efficiency:

  • CloudMedX: Integrates AI with health data for predictive analytics and operational insights, improving patient management and efficiency.

    
    

  • Omega Healthcare (with UiPath): Automating administrative processes like billing and claims.

    
    

  
  

  Final Thoughts: Navigating the Future of AI in Healthcare

  
  

  AI is not merely augmenting healthcare—it is changing and disrupting the face of healthcare industry. The fusion of LLMs, cloud/edge infrastructure, medical wearable devices, medical edge platforms, and smart data pipelines is unlocking a new era of scalable, cost effective, personalized, and proactive care.

  
  

  The next 5–10 years will see a largescale infusion of AI based technologies across the healthcare industry across the various regions.

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