150 Models and Counting: Your Guide to Generative AI Models for Healthcare and Life Sciences

Generative AI is already reshaping the Healthcare and Life Sciences (HCLS) industry. From AstraZeneca and Pfizer to 3M and the Allen Institute, innovators are using AI to accelerate drug discovery, boost scientist productivity, and streamline clinical workflows. As adoption grows, so does the need for models tailored to HCLS-specific challenges—from medical text summarization to DICOM image classification to improving efficacy of drug discovery. Yet, many organizations still rely on word of mouth or industry events to identify the right models, creating both friction and delays in the AI development lifecycle.

That’s where the Amazon Web Services (AWS) Marketplace excels. As the largest curated catalog of healthcare and life sciences AI models, it offers pre-approved enterprise procurement to streamline deployment at scale, quicken discovery of industry-specific models, and integrate quickly into clinical, research, or operational pipelines. Whether you’re aiming to improve cohort selection, accelerate drug development, or augment imaging workflows, AWS Marketplace makes it effortless to adopt the right generative AI model—fast.

More customers are moving toward the adoption of Agentic AI to enable autonomous workflows across the HCLS value chain. Utilizing the right model can enable the rapid deployment of agents for intelligent context-specific decision-making without the need to train models.

We’ll highlight some of the top fit-for-purpose generative AI models available today on AWS Marketplace, and explore the AWS services that help operationalize AI at scale in HCLS environments.

Finding the right model

With many organizations having laid the groundwork for generative AI adoption, we’re seeing a clear shift. Customers are increasingly turning to industry-specific models to address more complex challenges. Some customers are also looking to enhance existing applications with domain-specific models that offer more precision, improved safety, and better outcomes. These models are purpose-built for the unique demands of healthcare and life sciences, enabling faster insights, streamlined operations, and more effective interventions.

Following are some standout examples of how leading organizations are leveraging these specialized models (all available today on AWS Marketplace):

• Bio-FMs for novel drug identification: Generative AI models can help predict key absorption, distribution, metabolism, and excretion (ADME) properties of drug candidates. By analyzing molecular structures and physicochemical data, these models can forecast how a compound will behave in the body—reducing reliance on costly in vitro and in vivo testing. Evolutionary Scales’ ESMC models are examples of such models.
• Medical affairs and patient safety: Generative AI is playing a critical role in clinical trial safety monitoring, and particularly in identifying potential adverse drug reactions (ADRs). These models provide explainable insights that help teams take proactive measures to protect patients and comply with regulatory standards. The John Snow Labs Adverse Drug Events (ADE) model is trained on over 400 medical entity types and over 100 drug-related relationships, enabling it to accurately detect ADRs in unstructured data sources (including social media, clinical notes, and forums). This makes it a powerful tool for pharmacovigilance teams seeking to monitor signals beyond traditional reporting channels.
• Clinical trial design: QuantHealth models predict clinical trial endpoints and trial enrollment, which biotech companies can integrate directly into their trial design workflows. QuantHealth has a broad library of clinical development models trained on millions of patient lives. These models predict safety, efficacy, and probability of success with 85 percent accuracy, in oncology, cardiovascular, and autoimmune disease, for biologics, small molecules, and gene therapies. Their clinical operations models can accurately predict enrollment for any trial or site, globally. These models will be available on AWS Marketplace in May 2025.
• Clinical trial optimization: Life sciences organizations are increasingly turning to large language models (LLMs) on Amazon Bedrock, such as Anthropic’s Claude, to streamline the creation of clinical trial documentation. Their models can automatically generate key materials like concept sheets, randomized controlled trial (RCT) protocols, and more—reducing manual effort and accelerating trial setup timelines. By automating time-consuming documentation, generative AI helps research teams focus on study design, patient recruitment, and regulatory strategy—ultimately accelerating the journey from concept to clinic.
• Computer vision models in clinical settings: Computer vision models in healthcare are being used for a range of applications—from monitoring PPE adherence to providing auxiliary diagnostic support. One example is the Diabetic Retinopathy Detector model from VITech Lab. This image analysis and anomaly detection model identifies and classifies diabetic anomalies in retinal scans. By ranking cases by severity, it helps clinicians prioritize urgent cases, enabling faster diagnoses and early intervention to prevent vision loss.
• Pathology image analysis to early cancer detection: The Bioptimus H-Optimus-O model is the world’s largest foundation model for pathology, featuring 1.1 billion parameters and trained on 500,000 whole slide images (WSIs). This model enables patch-level classification of pathology slides, helping detect potentially cancerous cells with high accuracy and speed. By automating and enhancing the analysis of complex histopathology data, Bioptimus supports early cancer detection, reduces diagnostic delays, and assists pathologists in managing rising caseloads.
• Medical summarization for clinical workflows: Generative AI models like the John Snow Medical LLMs are purpose-built for healthcare-specific tasks, such as summarizing discharge notes, radiology reports, pathology results, and other complex clinical documents. These models can also answer targeted questions about a patient’s principal diagnosis, tests ordered, or a research study’s design and outcomes. This can help clinicians navigate dense unstructured data.
• AI assisted diagnosis and treatment planning: Palmyra-Med 70B is a large language model (LLM) developed by Writer, designed specifically to address the needs of the healthcare industry. It excels in biomedical benchmarks, achieving an impressive average score of 85.87 percent—outperforming models like GPT-4, Claude Opus, Gemini, and Med-PaLM-2, as well as a medically-trained human test-taker. It enhances clinical decision-making by performing advanced entity recognition, extracting medical concepts such as diseases, medications, procedures, and anatomical terms from unstructured text. Both the John Snow Medical LLMs (alongside over 40 other models) and Palmyra-Med-70B are available on AWS Marketplace, making it straightforward to integrate them into existing clinical applications and workflows.

Discover, fine-tune, and deploy generative AI models in HCLS

Once you’ve identified the right generative AI model for your HCLS needs, AWS provides a full suite of tools to help you discover, fine-tune, and deploy these models within a secure, compliant, and scalable environment. From foundational data storage to purpose-built development platforms, AWS supports every stage of your generative AI journey. Following is how AWS can accelerate generative AI adoption in your organization.

Amazon SageMaker Unified Studio – Complete model development and fine tuning

Amazon SageMaker Unified Studio offers a unified, integrated environment designed specifically for building and fine-tuning models in the HCLS domain. It brings together all the tools needed to manage data, build models, and develop generative AI applications—all within a single, governed environment. It integrates seamlessly with services like Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker machine learning (ML) services, empowering teams to:

• Fine-tune models using HCLS-specific datasets
• Process multimodal data (structured, text, image, omics)
• Develop and deploy complete generative AI applications

Amazon Bedrock – Rapid model inference and evaluation

Amazon Bedrock is ideal for quickly deploying and operationalizing pre-trained foundation models (FMs) without managing infrastructure. With Amazon Bedrock and the AWS Marketplace, developers can now discover, test, and integrate over 160 specialized and emerging FMs, including those tailored for healthcare use cases. Key features include:

• Amazon Bedrock Evaluations: rapidly assess and compare models to find the best fit
• API-based access to top models from leading providers
• Seamless integration with SageMaker and other AWS tools for downstream workflows

AWS HealthOmics – Accelerating Scientific Discovery

AWS HealthOmics accelerates biological insights with managed data stores and workflow automation. Its Ready2Run workflows (developed by industry leaders like NVIDIA, Sentieon, and Element Biosciences) support genomics and proteomics pipelines, including:

• Broad Institute’s GATK Best Practices
• AlphaFold for protein structure prediction
• Proprietary drug discovery pipelines

These workflows enable researchers to train and deploy leading-edge models without building pipelines from scratch. This helps accelerate everything from gene function discovery to novel therapy development.

AWS Storage Solutions – Scalable, Purpose-Built Data Infrastructure

Effective generative AI starts with the right data foundation. AWS offers scalable, secure storage options that cater to the diverse data types used in HCLS:

• Amazon Simple Storage Service (Amazon S3): A flexible, general-purpose option for storing structured and unstructured data
• AWS HealthImaging: Purpose-built for cloud-focused medical imaging applications
• AWS HealthOmics: Optimized for storing and analyzing omics datasets

These storage services integrate directly with Amazon Bedrock and SageMaker, enabling seamless model training and inference across multimodal data types.

Together, these AWS services create a robust suite of services that streamlines and accelerates the deployment of generative AI across the healthcare and life sciences value chain. Whether you’re developing custom models, integrating third-party solutions, or deploying intelligent agents for clinical decision-making, AWS offers the flexibility and scalability to make it happen.

Conclusion

We explored key Healthcare and Life Sciences use cases where industry-specific generative AI models can deliver measurable improvements in both business performance and patient outcomes. These benefits are made possible by the comprehensive suite of AWS services purpose-built to support generative AI—from data processing and model fine-tuning to secure deployment and inference.

With tools like Amazon SageMaker and Amazon Bedrock, AWS offers one of the largest selections of models tailored to HCLS challenges, enabling organizations to build, scale, and operationalize generative AI solutions more effectively than ever.

Ready to get started? Next steps:

• Explore our portfolio of generative AI models and healthcare-specific solutions on AWS Marketplace
• Engage with AWS teams to identify the right models aligned to your organization’s business needs and clinical goals
• Collaborate with AWS Solution Architects or trusted AWS Healthcare and Life Sciences Partners to design and implement scalable architectures tailored to your HCLS use case

Generative AI is already reshaping Healthcare and Life Sciences—let AWS help you lead the way. Contact an AWS Healthcare or Life Sciences Representative today.

Learn more:

• Pre-training genomic language models using AWS HealthOmics and Amazon SageMaker
• John Snow Labs Medical LLMs are now available in Amazon SageMaker JumpStart
• Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0.