When properly executed, a healthcare AI product will always provide more value than the development costs. Our partner, who works in AI medical coding, has discovered that their AI platform could recover $1.14M annually in revenue lost due to human error in coding. That’s only one instance based on a typical medical practice audit.
As long as the derived value is greater than the cost of developing AI in healthcare, we’ll likely see more uses for this new technology.
The cost of AI in healthcare differs depending on the nature of the AI models, the amount of data processing, and the integration requirements to the existing healthcare systems. Here is a thorough analysis of the cost associated with various AI solutions for healthcare.
| AI Model Type | Estimated Cost ($) | Development Time (Months) | Description |
| Machine Learning (ML) Algorithms | $150,000 – $200,000 | 3 to 6 months | Helpful to aid in the purpose of predictive analytics risk-score and data classification. |
| Neural Networks | $200,000 – $300,000+ | 6 – 9+ month | Excellent for pattern recognition with complex patterns and diagnostic tools. |
| Generative AI (LLM Models) | $250,000 – $500,000+ | 6-12+ months | Make use of large-scale language models for chatbot and clinical documentation. |
| Computer Vision Models | $180,000 – $400,000+ | 6-12 months | The centre is designed for video or image-based diagnostics, including pathology and radiology. |
| AI-Powered RPA (Robotic Process Automation) | $100,000 – $250,000 | 3 to 6 months | Improves administrative tasks such as billing and intake. |
| Overall Cost Range | $100,000 – $500,000+ | 3 – 12+ Months | Varieties based on the scope of use and compliance with requirements. |
The price of developing AI in healthcare varies between $100,000 and $500,000+, based on the kind of AI solution. Generative AI algorithms and systems for computer vision usually require the most budgets and the most protracted timeframes.
AI-Associated R&D Costs
As we have discussed in previous blog posts, AI development differs slightly from conventional software project development. This is mostly due to the focus on developing and researching.
In simple terms, we might not always get a satisfactory result from the AI model, or in other words, we might not always get an acceptable result in a straight line. AI is a process that requires time to try and improve results. The founders must consider certain aspects when evaluating ML-related R&D expenses.
Cross-functional teams
While a typical healthcare service needs back-end and front-end designers, engineers, testers, and project managers, an AI product will require the participation of ML experts and data scientists. These are costly hires, and they are accountable for designing data models and preparing data for analysis.
Scalable technologies
The other thing founders have to consider is selecting the appropriate technology stack that is precisely what it promises and is scalable far beyond the immediate MVP requirements. This is a very R&D-oriented process by itself.
I can remember when we had to dig deep to find the best technology ensemble to accommodate the Allheartz product in a logical technology stack. This included analyzing the open-source and off-the-shelf data models, libraries, and development frameworks for ML, etc.
Technical debt
Ultimately, it is up to the entrepreneurs to ensure their structure is not at risk of tech debt, a prevalent problem. Every software development project will eventually run into the limits of tech debt. It’s just a matter of time since technology becomes obsolete with time.
However, we can stop this negative effect by properly setting up the development process. For instance, an AI project should have carefully installed DevOps pipelines for data. Another option is to acknowledge the debt to technology early, prioritizing it and making it pay as quickly as possible with the available resources.