Building Secure AI Models for HealthTech

As healthcare increasingly embraces AI innovation, building secure and reliable systems becomes paramount. Diagnostics and medical solutions require high reliability and predictable performance. Every solution must be accurate, precise, and prioritize safety above all.

Drawing from our experience in implementing healthcare AI systems that serve thousands of patients daily, we'll explore the systematic approaches that ensure both innovation and safety.

Many Faces of AI in Healthcare

AI applications in healthtech span multiple areas, from chatbots and AI assistants powered by large language models (LLMs) that support patient interactions and initial screening, through machine vision models analyzing medical scans, to predictive models, and classifiers powered by classical machine learning architectures.

Each of these applications has its own challenges to overcome, but one overarching requirement remains constant: safety. In healthcare, the cost of mistakes is exceptionally high.

The Human in the System

The fundamental principle guiding safe AI in healthtech is straightforward—AI solutions should support medical personnel rather than replace them.

In practice, this means we can automate data analysis, symptom checking, and similar tasks, but the final decision must rest with human experts. We are building decision support systems, not replacements.

This approach aligns with relevant regulations, such as the EU AI Act, but should be adopted regardless of regulatory requirements, given its inherent safety benefits. 

Consider AI a second opinion system—one that minimizes the probability of errors while supporting decision-making personnel, reducing their administrative workload and allowing more time for critical decisions and patient care.

Explainable Artificial Intelligence

In sensitive fields such as healthtech, understanding how a conclusion was reached often carries equal importance to the conclusion itself. Trusting raw outputs from an AI system without understanding the factors influencing its decision creates significant risks.

The solution lies in selecting appropriate explainable and deterministic AI models.

This approach relies on stable machine learning models that provide consistent answers across similar inputs, with interpretable outputs wherever possible.

Neural Networks and Transparency 

For advanced AI models, tracing the exact decision path can be challenging—millions or billions of parameters make visualization and tracing nearly impossible.

This explains why classical machine learning excels in medical tasks like symptom diagnostics. It offers comparable performance without the complexity and opacity associated with neural networks.

However, this doesn't preclude the use of advanced AI. Rather, it emphasizes the importance of selecting the most appropriate tool for each specific task.

Machine vision, particularly in medical imaging analysis, demonstrates this principle perfectly. Here, advanced vision models achieve detection levels exceeding human evaluation.

Furthermore, these AI models can precisely outline areas of interest on images, and highlight detected anomalies. This capability provides the perfect level of explainability, which enables the system to function as an effective assisted vision tool for technicians.

AI Chatbots in Healthcare

Standard AI chatbot models fall short of meeting healthcare sector requirements. The underlying language models (LLMs) lack transparent decision-making processes, leading to unpredictable behaviors that undermine trust.

Combined with their tendency to generate unsubstantiated outputs (known as model hallucinations), this creates an unacceptable risk for healthcare applications.

However, several strategies can mitigate these risks. Chatbots can be enhanced with specialized knowledge bases—Retrieval Augmented Generation (RAG) systems—that serve as reliable sources of truth.

Additionally, implementing rigorous content filtering for both input and output messages helps monitor potentially problematic incoming messages and verify chatbot response quality, triggering new response generation when necessary.

Finally, specialized tasks such as classification or computations can be handled by dedicated models connected to the main LLM, providing required precision when needed.

Agentic Workflow

This leads to an agentic workflow—an AI decision engine that uses an LLM to analyze user input and generate responses while relying on specialized tools for specific operations.

This approach combines the flexibility of generative LLM models with the precision and explainability of standard machine learning models, creating a comprehensive tool that functions as a medical assistant—efficiently handling tasks from dataset analysis to appointment scheduling and documentation summarization.

Implementation Considerations

As AI capabilities evolve, successful implementation increasingly depends on balancing innovation with proven safety practices. Based on our experience implementing AI in healthcare settings, several key factors ensure success:

1. Safety First

• Establish clear safety requirements
• Implement robust verification systems
• Maintain continuous monitoring
• Document all safety measures

2. Model Selection

• Choose explainable models where feasible
• Match AI capabilities to specific use cases
• Implement appropriate safety mechanisms
• Ensure scalability without compromising security

Security Checklist

□ Data encryption at rest and in transit 

□ Regular security audits 

□ Access control and authentication 

□ Comprehensive audit logging 

□ Privacy-preserving AI techniques 

□ Regular model monitoring and retraining

Conclusion

Security in healthcare AI isn't just a technical requirement—it's a commitment to patient care and medical excellence. Success depends on selecting appropriate tools for specific tasks and implementing explainable, transparent, and stable AI systems that operate under direct supervision.

As you build your healthcare AI solutions, focus on creating systems that augment and support medical personnel while maintaining unwavering safety standards. This approach ensures both technological advancement and, most importantly, improved patient care.