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Effective AI Prompting Strategies for Healthcare Applications

Woman interacting with a transparent digital interface, illustrating healthcare AI applications and precision-driven prompting strategies in clinical settings.
Author
Filip Begiełło
Published
July 1, 2025
Last update
July 1, 2025
Woman interacting with a transparent digital interface, illustrating healthcare AI applications and precision-driven prompting strategies in clinical settings.

Table of Contents

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Key Takeaways

  1. Building safe AI for healthcare starts with crafting precise prompts that leave no room for ambiguity.
  2. Model hallucinations aren’t just a technical glitch—they can quietly break healthcare apps if we don’t design for trust.
  3. Multi-agent systems can split complex healthcare tasks across specialized AI agents to keep large language models focused and reliable.
  4. Guardrails and input filters aren’t optional—they’re essential to keep AI responses aligned, compliant, and safe for patient-facing applications.
  5. Retrieval-augmented generation (RAG) connects AI answers to real, verified data, closing the gap between language models and medical reality.
  6. AI can be safely implemented in regulated healthcare environments—if you know which strategies work and which traps to avoid.

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LLMs are now widespread and offer a plethora of benefits—this much we already know. We see dynamic adoption across many industries and use cases, including information aggregation, smart searches, user interactions, and more.

But there is one field that, despite its massive potential gains, is still slower to adopt such new tools—healthtech.

The Challenge Ahead

The specific realities of healthtech and healthcare pose a unique challenge for GenAI in general, and especially for the use of LLMs. Here, the margin for error is slim, data is highly sensitive, and the potential cost of mistakes can be severe.

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LLM M.D.: Why Accuracy and Trust Are Critical

At first glance, LLMs appear to be highly effective in the medical domain. Researchers have even shown that fine-tuned LLMs can outperform first-contact physicians—not just in diagnostic accuracy, but surprisingly, in empathy as well.

You might think examples like these would make LLMs a clear fit for healthcare, but there’s much more nuance.

Understanding Model Hallucinations and Trust Gaps

The first concern is the reliability of the model’s knowledge and answers. LLMs are trained on vast datasets that often contain conflicting data points. Crucially, we have no way of knowing which parts of this internal knowledge the model uses in its responses. This can result in seemingly coherent, but completely fabricated, answers—hallucinations.

These risks are not confined to diagnostic use cases. Even simpler tasks, such as document summarization, can lead to dangerous misinterpretations or incorrect outputs.

Responsibility and the Opacity Problem

When a human makes a decision, they take responsibility for it. We accept a margin of error because we trust their expertise. With AI models, the expectation is different. We demand higher accuracy and do not tolerate the idea that a model can "misjudge" something.

The opacity of LLMs—their black-box nature—only deepens this trust gap, eroding confidence instead of building it.

Data Privacy and Safety

A key concern in healthtech is data privacy. We often work with personally identifiable information (PII), which is strictly regulated under frameworks like HIPAA and GDPR. This data requires exceptional care.

For LLMs, this creates a significant obstacle. Models need to process data—whether patient inputs, internal documents, or other sensitive sources. Ideally, we could deploy LLMs on private servers with fully controlled security environments. Unfortunately, the computing power required to run high-performing LLMs makes this nearly impossible in most cases.

To complicate matters further, many advanced models are closed-source, meaning that even with the proper hardware, you cannot run a private instance.

This often leaves healthcare companies in an uncomfortable position: using third-party LLMs that may process sensitive data in different legal jurisdictions. In the context of healthtech, this is highly questionable.

Five transparent capsules displaying programming code, representing multi-agent systems and complex AI workflows in healthcare technology.

How to Build Safe, Reliable LLM Systems for HealthTech

Despite these barriers, LLMs can be safely and effectively used in healthcare. It’s not about avoiding them entirely—it’s about building the right systems, using the right techniques.

The Art of AI Prompting for Healthcare Apps

Some issues with model outputs and response quality can be addressed—just as with any LLM—through prompt engineering. This is the most basic, yet most essential, part of building safe LLM systems.

While many general best practices apply (be precise, avoid verbosity, eliminate ambiguity), healthtech demands additional rigor.

Focus on Precision and Hard Data in Healthcare AI Prompts

In healthcare, precision is non-negotiable. You need to include the most relevant, hard data in your prompts—either as direct instructions or dynamic context.

You can dynamically format the prompt, depending on the state of the system, always passing hard data relevant to the query or operations. The more accurate context you provide, the more on-topic the answer will be.

Try, for example, parsing and introducing patient info into the context when building any solutions reasoning over patient documentation. Or define specifics of work and context of use for LLMs operated by specialist medical practitioners—defining that we navigate in terms commonly used by, let’s say, radiologists removes a level of ambiguity and double meaning, setting a hard context for the LLM.

The more accurate the context, the more relevant and reliable the response.

Addressing Model Certainty in Healthcare AI Responses

LLMs tend to respond with absolute certainty—even when unsupported by facts. In medical systems, this is not only misleading but may also be unethical or illegal, especially in regions where AI-generated diagnoses are not permitted.

Techniques like using structured prompts, markdown formatting, or the ReAct prompt pattern (which enables reasoning loops) can help manage this. These methods standardize model outputs and make the system safer.

Delegating Complex Tasks: Multi-Agent Systems in Healthcare AI

Aspects of prompting lead us straight into another point—thinking more towards multi-agent systems, utilizing query engines, specialized tools, and delegate agents.

Splitting Work Across Specialized AI Agents in HealthTech

When performing a complex and long task, LLMs tend to lose focus—this is where most of the information is lost, and most hallucinations are introduced. Moreover, as discussed earlier, successful prompts are precise and exact—this often clashes with complex tasks that require performing many different operations, following different sets of rules. When mixed like that, some spillover is inevitable.

The best approach is to split such tasks into specialized tools, for example:

  • A main agent interacts with the clinician.
  • An image interpreter processes scan data.
  • An analytical engine handles tabular data.
  • Sub-agents retrieve data from EHR systems.

Only after receiving all the data perspectives do they go over the results of the AI tools.

This multi-agent flow mirrors the architecture of advanced systems like modern ChatGPT or Perplexity but is fine-tuned for specific healthcare tasks. It allows you to control each step and ensure thorough analysis.

Digital capsule displaying an explanation of large language models, emphasizing AI accuracy, model behavior, and safe healthcare AI implementation.

Model Guardrails: Keeping Healthcare AI Safe

On the topic of specialized LLM engines working on smaller parts of the system, a special case would be the use of so-called guardrail models—essentially input and output filters powered by LLMs. Here, our goal is to ensure that nothing conflicting with our instructions exits the model and nothing potentially harmful enters the model.

Input Filters: Moderators for Safer Inputs

Starting with input filters—moderators. These engines aim to filter user inputs and block obviously harmful messages: explicit content, attempts to bypass the system, and so on. They also filter out joke inputs or off-topic questions and instructions.

The first part is obvious, but the second might not be. Consider a patient-facing LLM asked about a controversial topic—you’ll quickly see why this matters. Any answer would not only be unrelated to the use case but also potentially damaging.

In LLM systems, conversation history is usually still held in context, meaning that any such answers might pollute future generations, leading to harmful consequences.

Output Filters: Guardrails for Safer Responses

While moderators might be intuitive enough, guardrails—or output filters—might not be. After all, why parse what we’ve just generated?

This ties directly to the principle of work splitting. While the main agent might be optimized for reasoning, trying to steer it towards a specific tone of voice can result in directionless prompting. It is often better to focus the core system on the task at hand and leave response refinement to these guardrails.

For example, we can ensure that a therapeutic chatbot maintains the right tone of voice and never slips up by reformatting the answer, cutting out unwanted parts, or forcing a new generation in extreme cases. This design pattern results in not only more accurate but also more natural-sounding LLMs.

Cite Your Sources: Increasing Transparency

We’ve talked about query engines, document interpretation, and tools for summarization, but those elements do not solve the issue of opacity that LLMs introduce. What’s more, the introduction of multiple tools and agents might obscure the decision process even further. It seems that by solving one issue, we’ve created a new one.

Here, the answer might be easier than you think.

The Power of Retrieval-Augmented Generation (RAG) in Healthcare

The solution is RAG—retrieval augmented generation—a simple yet elegant method for providing exact context matching current queries. This approach assumes building a vector database populated with hand-compiled documents, acting as a verified and coherent knowledge base for the LLM.

RAG systems connect the base AI agent with this knowledge base, allowing it to query over the database and retrieve the most relevant parts in response to the input. By combining such a tool with strict prompting—explicitly instructing the model to answer only in the context of retrieved information—we reduce hallucination or misinterpretation possibilities to a minimum.

RAG and semantic searches deserve an article of their own, but for now, it’s enough to understand that we have a wide range of tools and algorithms not only for retrieving this data but also for matching and processing it before it even reaches the main agent.

We can search by:

  • Cosine similarity: providing high adaptability and flexibility.
  • Keywords or matching/ranking functions like BM25: gaining accuracy and hard matches.
  • Hybrid approaches: combining elements of both methods.
Glass capsules with reflected AI system commands, highlighting data processing, reasoning loops, and safe AI design in healthcare apps.

Closing the Opacity Gap: Using Citations

The final point here is citation itself—a simple mechanism that directly addresses the opacity problem we started with.

With RAG systems, we can easily show what sources were used to generate the final response. Simply append the retrieved chunks—along with the information on where they were originally sourced—at the end of the response, just like in an academic paper.

This way, the end user can easily verify whether the source really supports what the LLM claims.

Similarly, when parsing input documents, you can point to specific parts used to generate corresponding parts of the summary—resolving the issue of interacting with a black box.

Safe Data is Anonymous Data

So far, we’ve covered ways to manage LLMs to reduce hallucination, ground answers in reality, and use other safety techniques. But as we laid out in the beginning, all this is often constrained by a significant barrier—data safety.

The first thing to say here is that, more often than not, a solid legal framework will guarantee enough safety for your use cases, even in healthcare settings.

Many successful healthtech solutions are based on IaaS (Infrastructure as a Service) environments—private instances like AWS or Azure. With the proper setup, you are operating in a safe and compliant environment because you can define what data policies apply and where the instance is located.

Understanding Model-as-a-Service (MaaS) Solutions

Similar principles apply when it comes to MaaS (Model as a Service) solutions, where you access LLMs through the provided API—albeit with some crucial distinctions.

Most large providers like OpenAI offer BAA agreements for HIPAA or zero data retention and data residency options aimed more at GDPR regulations. This is a solid start.

The concern about models being trained on your inputs, using and storing your data, is also addressed in most cases. MaaS providers clearly define in their terms what will be used and how—usually guaranteeing that API-processed data will only be used for processing, not for training.

Options for Handling Delicate Data

For the most delicate use cases, there are still options.

The most straightforward is to host your own instance of an LLM model, but as noted earlier, this limits the model selection and is often prohibitively costly.

This brings us to the core of this section—data anonymization. This approach is flexible enough to offer both the benefits of MaaS solutions and the safety level provided by fully controlled infrastructure.

After all, if you do not send any personally identifiable information (PII), then no data can be leaked. But how do we achieve this?

Simply removing PII elements like names or addresses will often hurt accuracy, as we are removing a layer of information.

Close-up of a digital capsule with the question 'How do large language models work?' glowing on the surface, symbolizing AI comprehension in healthcare applications.

How Anonymization Works in Healthcare AI Systems

To solve this, we can add an additional processing step between our system and the target LLM—the anonymization service.

Every outgoing data point—documents, user inputs, or anything else—is parsed using dedicated ML models and NLP techniques to identify potential entities defined as private information. These entities are replaced with labels; for example, “John Doe” might become “PERSON_1.” This mapping is safely stored on our end, and sanitized data is sent to the LLM.

Once we receive the results back, we can simply replace each label with the matching name.

It sounds simple and elegant, but there’s a small catch.

This approach, depending on the method used, might not be absolutely accurate—we rely on models and algorithmic analysis to find all the PII. While this might be acceptable in some cases, in others we need an absolute guarantee of no data leakage.

One way to achieve this is to pass the responsibility for redacting the information to the personnel interacting with the system—either by defining a set list of entities to be anonymized or by manually replacing the names. It might be tedious and less elegant, but it reduces the risk of accidental PII slipping past the automated measures.

Safe LLMs in HealthTech: How Rather Than If

When we review all the aspects discussed, a clear picture emerges: LLMs can be safely used even in highly sensitive areas like healthtech.

The problem is not with the technology itself but with the lack of expertise and proper approaches. Many of the apparent barriers can be eliminated with the right strategies.

That’s exactly why we created the AI Implementation in Healthcare Masterclass—to help healthtech teams apply these strategies in the real world, safely and effectively.

If you’re building with AI in healthcare, this is where you’ll find the practical guidance to do it right.

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Frequently Asked Questions

What are the most effective AI prompting strategies for healthcare?

The most effective AI prompting strategies for healthcare focus on precision, context, and safety. They involve crafting structured, specific prompts that guide large language models (LLMs) to deliver reliable and accurate healthcare responses. Techniques like multi-agent systems, retrieval-augmented generation (RAG), and guardrail models help reduce hallucinations and ensure compliance with healthcare regulations.

How can AI prompting improve patient safety in healthcare apps?

AI prompting can improve patient safety by reducing model hallucinations, enforcing strict input and output filters, and ensuring that AI-generated answers in healthcare apps are grounded in verified medical data. Momentum’s approach to AI implementation includes building safe prompting workflows that protect patient data and align with HIPAA and GDPR requirements.

What are multi-agent systems in healthcare AI and why do they matter?

Multi-agent systems in healthcare AI divide complex tasks across specialized agents, such as language models, data retrieval engines, and image interpreters. This strategy helps prevent AI errors, supports more accurate decision-making, and is a key part of Momentum’s AI implementation framework for healthcare applications.

How do guardrails make AI safer for healthcare applications?

Guardrails in AI for healthcare include input filters and output filters that block unsafe, off-topic, or non-compliant data from entering or exiting the AI system. Momentum integrates these guardrails to maintain control over AI-generated content and to ensure safer patient interactions in digital health products.

Why is data anonymization critical in AI-powered healthcare solutions?

Data anonymization is essential for protecting personally identifiable information (PII) when using AI in healthcare. Momentum’s AI strategies for healthtech emphasize anonymization techniques that allow AI models to process data securely without compromising patient privacy. This is crucial for using advanced AI models like GPT or Google Cloud’s AI APIs in healthcare environments.

How does retrieval-augmented generation (RAG) improve AI accuracy in healthtech?

RAG improves AI accuracy in healthtech by connecting large language models to verified, curated databases. This ensures that healthcare AI responses are based on real, up-to-date sources rather than general internet training data. Momentum leverages RAG systems to build trustworthy, compliant healthcare AI applications.

What makes Momentum a trusted partner for healthcare AI implementation?

Momentum specializes in building compliant, scalable AI solutions for digital health. With proven expertise in AI prompting strategies, multi-agent architectures, safe data handling, and guardrail deployment, Momentum helps healthtech companies implement AI safely—whether you’re working with Google AI, ChatGPT, Perplexity, or custom LLMs.

Can AI be safely implemented in regulated healthcare products?

Yes. With the right prompting strategies, multi-agent systems, strict guardrails, and data anonymization, AI can be safely implemented in regulated healthcare products. Momentum’s AI Implementation in Healthcare Masterclass provides a practical, step-by-step framework to help healthtech teams build safe, compliant AI-powered solutions.

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Looking for a partner who not only understands your challenges but anticipates your future needs? Get in touch, and let’s build something extraordinary in the world of digital health.

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Written by Filip Begiełło

Lead Machine Learning Engineer
He specializes in developing secure and compliant AI solutions for the healthcare sector. With a strong background in artificial intelligence and cognitive science, Filip focuses on integrating advanced machine learning models into healthtech applications, ensuring they adhere to stringent regulations like HIPAA.

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