The Cost of AI in Healthcare Explained: Is It Worth It?
Healthcare organizations face many AI opportunities: drug discovery, clinical documentation, revenue cycle management, and digital assistants in insurance call centers. Each offers benefits but comes with its own price tag. Beyond the obvious costs, hidden expenses like change management and ongoing maintenance can add up.
Understanding the total cost helps healthcare leaders choose AI projects with the best return on investment (ROI). Below are eight key cost factors to consider when planning AI initiatives.
1. Building an AI Team
Most healthcare entities need a dedicated team to lead AI efforts—sometimes called an AI center of excellence or governance group. This team sets strategy, identifies use cases, and oversees projects. Salaries for roles such as AI directors, project managers, data scientists, and ethicists make up a significant portion of costs. Even a small team can cost around $500,000 annually.
The goal is to build trust and agility, enabling fast identification, deployment, and scaling of AI projects that deliver value, while quickly moving on from those that don’t.
2. Data Collection and Preparation
Preparing data is often the most challenging and costly AI task. Healthcare data is scattered across systems and formats, requiring cleansing, deduplication, and harmonization. Many organizations lack mature data governance programs, which are essential for compliance and effective AI use.
Investing in data leadership roles and modern cloud-based infrastructure is necessary. Cloud platforms like Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse charge based on usage, but prices are becoming more competitive.
3. Deployment Style
AI deployment costs vary widely. Custom-building a large language model (LLM) can start at $2 million and requires specialized infrastructure. More affordable options include fine-tuning third-party models with domain-specific data or using off-the-shelf generative AI tools like Google’s Gemini, OpenAI’s ChatGPT, or Microsoft’s Copilot products.
Off-the-shelf tools have lower upfront costs and faster deployment but less customization. They often charge per user, with fees around $25 to $40 monthly.
4. Model Complexity
Larger AI models usually mean higher costs, but open-source alternatives like DeepSeek R1 have shown that powerful models can be built more affordably. Open-source platforms reduce tokenization fees, which are usage-based costs charged by many commercial AI services.
This shift could make AI more accessible to healthcare providers who previously relied heavily on vendors.
5. Integration with Existing Systems
Healthcare systems often run on outdated, siloed technology. Integrating AI with these legacy systems requires custom work, driving up costs. Modernizing electronic health records and ensuring interoperability adds to the investment.
Data from wearables and IoT devices further complicates integration, requiring extensive preprocessing and strict security measures to comply with HIPAA and other regulations.
6. Compliance and Risk Management
Healthcare AI must adhere to regulations such as HIPAA, FDA 21 CFR Part 11, and GDPR. Organizations invest in governance structures, ethics committees, and security protocols to manage compliance and minimize risks. Security needs to be built into AI development from the start.
7. Change Management and Training
Change management is often underestimated but critical for AI adoption. It involves preparing staff for new workflows and technologies. Training costs can be significant—sometimes three times the development cost.
Training must cover frontline clinicians as well as administrative staff to ensure smooth integration and maximize AI benefits.
8. Ongoing Operations and Maintenance
AI projects require continuous monitoring, retraining, and tuning to maintain accuracy. Off-the-shelf AI tools incur ongoing usage fees, while custom models need regular updates. Maintenance costs may rise with system scale but could decrease as more efficient technologies emerge.
Estimating AI Project Costs in Healthcare
AI project costs vary based on complexity, data readiness, and integration needs. A simple prototype may cost between $150,000 and $200,000, but full production integration can multiply that amount.
More complex projects often start above $1 million. Training large language models can reach hundreds of millions in costs. Besides development, hidden expenses like cloud usage and maintenance must be accounted for.
Costs tend to decrease as organizations build on existing AI projects. Subsequent initiatives using the same data sets can cost 30% to 40% less.
How AI Can Reduce Healthcare Costs
Despite upfront expenses, AI can save money in areas like revenue cycle management. For example, AI-generated appeal letters for denied insurance authorizations can recover significant revenue for providers.
Payers use AI for claims processing and fraud detection, offsetting AI costs. Other promising applications include medical imaging and chronic disease management with wearables, which improve outcomes and reduce hospital stays.
Weighing AI’s Value
Evaluating AI projects requires looking at both value capture—improving current operations—and value creation—finding new revenue streams. ROI should consider financial gains, user experience, and satisfaction.
Some projects deliver across multiple areas. For instance, accelerating drug development can lead to cost savings and increased revenue. Clinical documentation tools can reduce clinician workload and improve work-life balance, benefiting both staff and patients.
One physician using AI note-taking software reported saving 5 to 15 minutes per patient, cutting after-hours documentation and allowing more focus on care.
For healthcare professionals seeking to build AI skills, exploring targeted AI courses by job role can provide practical knowledge to support AI adoption efforts.
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