Can You Trust AI in Flow Cytometry?

Artificial intelligence (AI) is rapidly changing the field of flow cytometry, offering faster and more precise data analysis. By automating complex processes, AI can help detect and classify cells in ways that were once only possible with expert human interpretation.

But as AI becomes more integrated into medical diagnostics, a critical question arises:

Can you trust an AI model that gives you different answers every time you analyze the same flow cytometry sample?

Clinicians and researchers rely on flow cytometry for life-changing diagnoses, from immune disorders to blood cancers.

For AI to be truly valuable in this field, it must be both accurate and trustworthy. That means ensuring AI models provide consistent and reproducible results while also making their decision-making transparent and interpretable.

How is AI used in healthcare?

Artificial intelligence (AI) is changing the way we approach diagnostics by automating challenging tasks that used to require expert-level human interpretation.

In healthcare, AI is being used to analyze vast amounts of medical data, detect patterns, and support faster, more accurate decision-making.

From radiology to pathology, artificial intelligence is becoming an essential tool for increasing efficiency and precision.

What about AI in flow cytometry?

Flow cytometry is a powerful lab method used to analyze the characteristics of cells, often for diagnosing blood disorders, immune system conditions, and even cancer.

Traditionally, this method has required skilled specialists to manually identify cell populations, interpret results, and look for abnormalities. But with artificial intelligence (AI) in flow cytometry, most of this effort can be automated, standardized, and accelerated.

Here are some of the key ways AI is changing the flow cytometry process:

• Automates gating and classification of cell populations.
• Identifies patterns in complex data sets that might be missed by the human eye.
• Reduces variability between different analysts, ensuring more consistent results.
• Speeds up diagnosiswhich helps clinicians to respond faster to critical findings.

By integrating artificial intelligence into flow cytometry, laboratories can analyze samples more effectively, reduce human error, and provide more reliable, data-driven insights that support medical decision-making.

However, with these advancements come important questions:

Can AI be trusted to deliver results as accurately as human experts?

That’s where the discussion of determinism and interpretability begins.

Why do AI models need both determinism and interpretability?

AI is changing flow cytometry by making data analysis faster and more efficient. But speed alone isn’t enough.

Consistency is critical in medicine, and life-saving. But AI models often trade-off between accuracy and determinism. So, how do we guarantee the reliability in flow cytometry?

To be effective in medical diagnosis, AI must be consistent and explainable. This is where two fundamental concepts come into play.

• Determinism (consistency) → Guarantees that AI always gives the same output given the same input.
• Interpretability (transparency) → Guarantees that humans can understand how and why AI makes a decision.

Balancing these two is very important.

Deterministic AI builds reliability, while interpretable AI builds trust. However, achieving both in the same model comes with challenges.

Let’s break it down.

What is the role of determinism in flow cytometry?

If you run the same flow cytometry sample through an AI model twice, should you get the same result?

Determinism means that the AI will always provide identical outputs when given the same input. This is critical in medicine, where small inconsistencies can lead to misdiagnosis, delays in treatment, or loss of trust in AI-generated results.

Example of determinism in flow cytometry:

A deterministic AI model analysing leukemia samples will always classify cell populations in the same way, maintaining consistency across laboratories, operators, and test conditions.

Challenges of deterministic AI:

• Lack of flexibility → Deterministic models may struggle with new, unseen data or variations in sample quality.
• Potential overfitting → AI may become too rigid, limiting its ability to adapt to real-world variability.

Solutions to these challenges:

• Hybrid models combine deterministic decision rules with adaptive learning techniques.
• Controlled randomness introduces slight variations in training to improve robustness while maintaining output consistency.
• Strict validation protocols test AI models on many different kinds of datasets in order to guarantee their reliability in the real world.

Why is AI interpretability critical in medicine?

Would you trust an AI model that tells you a patient has leukemia but doesn’t explain why?

Interpretability ensures that clinicians understand how an AI model reached its decision. This is essential for regulatory approval, clinician trust, and real-world adoption.

Example of interpretability in flow cytometry:

An AI model analyzing immunophenotyping data should not just flag abnormal cell populations. It should highlight which biomarkers influenced the classification, allowing clinicians to verify and validate the results.

What makes AI interpretability difficult?

Many AI models operate as black boxes that provide accurate predictions but no explanation of how they arrived at those conclusions.

Complexity vs. transparency: Deep learning models are often highly accurate but difficult to interpret.

Solutions to these challenges

• Explainable AI (XAI) techniques and methods like feature importance analysis and visualization tools help break down AI decisions.
• Hybrid models combine rule-based systems with AI to create a more explainable decision-making process.
• Regulatory frameworks ensure AI models meet transparency requirements before being deployed in clinical settings.

While interpretability helps clinicians understand AI decisions, another challenge remains:

Should AI models always give the same result, or should they allow for some randomness that might produce slightly different results for the same input?

This brings us to the debate of stochastic vs. deterministic AI models.

Can AI be both reliable and stochastic?

Many AI models, particularly those based on complex algorithms like deep neural networks, incorporate elements of stochasticity.

Some AI models prioritize strict consistency, while others introduce controlled randomness to improve adaptability.

So, which is better for flow cytometry?

Stochasticity is randomness introduced during training (e.g., random initialization of weights or dropout techniques). While these stochastic processes can improve a model’s ability to generalize from training data, they also introduce variability in outcomes.

Advantages of stochastic AI:

• Helps prevent overfitting, making the model more robust.
• Allows AI to explore a broader solution space, improving generalization.

Disadvantages in medical AI:

• Even minor variability can reduce trust in AI-generated results.
• Clinicians expect reproducible outcomes—random changes in classification could compromise patient care.

The challenge in medical AI, especially in flow cytometry, is finding a balance between stochastic models (which improve adaptability) and deterministic models (which ensure reproducibility).

While they are fundamentally different, modern AI approaches combine elements of both to optimize performance in clinical settings.

How can hybrid AI models solve this problem?

To solve this tradeoff, researchers are building hybrid AI models that use:

• Deterministic core with stochastic flexibility → Models use fixed rules for critical medical decisions while allowing controlled randomness in areas where adaptability is beneficial.
• Ensemble learning → Instead of relying on a single AI model, multiple models work together to provide a more stable, reliable prediction while still capturing variability.
• Regularization & controlled noise → AI models introduce structured randomness during training to avoid overfitting, but once deployed, they operate in a deterministic manner for clinical use.
• Explainable AI (XAI) for transparency → Hybrid models integrate decision-tracking algorithms that make both stochastic and deterministic decisions interpretable for clinicians.

(Tip from Pol) What’s the ideal AI for flow cytometry?

AI in flow cytometry must be both interpretable enough to provide trust and effective enough to generalize across all patient sample types.

By using hybrid approaches, AI can retain the robustness of stochastic models while ensuring the trustworthiness of deterministic and interpretable decision-making, making it both clinically trustworthy and transparent.

What are the challenges of deterministic and interpretable models?

Achieving the right levels of determinism (reproducibility) and interpretability (transparency) isn’t easy. AI models designed for strict consistency may lack adaptability, while those built for interpretability may sacrifice performance.

In flow cytometry, where AI plays a critical role in medical decision-making, finding this balance means navigating three major tradeoffs:

1. Does AI have to choose between performance and transparency?

This is the accuracy dilemma.

Should AI models prioritize maximum accuracy or maximum transparency?

Highly complex deep learning models are frequently more accurate than simpler, deterministic ones. However, the trade-off is that these models are black boxes, which makes it impossible to understand why they make certain choices.

Challenge:

• In flow cytometry, raw accuracy is essential but not at the expense of transparency.
• Clinicians need to understand how AI reaches its conclusions, or they may not trust the results.

Solution:

• Use explainable AI (XAI) techniques to provide transparency without reducing accuracy.
• Combine deep learning with rule-based logic for interpretable decision-making.

2. Can AI be reproducible and still innovate?

If AI models are strictly deterministic, can they still adapt to complex medical tasks?

Strictly deterministic AI ensures consistent outputs, but it may struggle with new, complicated cases, limiting innovation. On the other hand, more flexible, adaptive AI models can generalize better, but their results may vary slightly depending on training conditions.

Challenge:

Improving determinism through controlled training conditions and reducing randomness improves reproducibility.

However, too much rigidity might prevent AI from learning new patterns, affecting its performance on diverse datasets.

Solution:

• Use hybrid AI approaches that balance structured determinism with adaptive learning.
• Implement controlled randomness that allows flexibility while maintaining reliable outputs.

3. Can AI be both complex and trustworthy?

How do we make AI models sophisticated enough to be powerful but simple enough to be trusted?

This is the clinician’s dilemma.

Medical AI must be trusted by clinicians and regulators, but the more complex an AI model becomes, the harder it is to validate, audit, and approve for clinical use.

Challenge:

Highly deterministic and interpretable AI models are easier to verify and validate.

However, simplifying AI models too much may lead to a loss of predictive power, which is critical for analyzing complex, multidimensional flow cytometry data.

Solution:

• Focus on regulatory-aligned AI development, making sure that AI meets approval standards without compromising performance.
• Develop user-friendly AI interfaces that allow clinicians to interact with models, inspect decisions, and make informed choices.

The ideal AI model should:

• Be deterministic enough to ensure consistency in results.
• Be interpretable enough to provide transparency in decision-making.
• Use explainability techniques to help clinicians understand AI-driven insights.
• Follow regulatory standards to ensure safety and reliability in real-world diagnostics.

The key features in determinism and interpretability

Can AI overcome these tradeoffs?

Balancing both determinism and interpretability is a constant challenge in medical AI.

Fortunately, through hybrid AI models, controlled randomness, and explainable AI techniques, developers are finding ways to deliver accurate, reproducible, and transparent AI for flow cytometry.

Why is trust the key to AI in medicine?

In medicine, a single incorrect AI-driven decision can have life-altering consequences. How do we ensure AI models in flow cytometry are both precise and transparent?

AI is changing flow cytometry by enabling faster, high-throughput data analysis. But its real impact depends on one crucial factor: TRUST.

Clinicians make life-critical decisions based on consistent, explainable results, so the appropriate level of determinism and interpretability is more than a technical concern. It's a medical need.

Understanding these tradeoffs is critical for three key stakeholders:

1. How can clinicians make better decisions with AI?

Building reliable models helps in designing AI systems that meet the strict demands of medical applications.

This guarantees that AI models are deterministic enough for consistency and also interpretable enough for transparency.

⚠️ Why it matters? An AI model that lacks trust will never make it to clinical use, no matter how advanced it is.

2. Trust is the bridge between AI and patient care

This can provide a clear understanding of how AI arrives at its conclusions and ensure AI outputs can be safely integrated into patient care workflows.

⚠️ Why it matters? If clinicians don’t trust AI recommendations, they won’t use them.

3. How can AI meet medical regulations?

The created AI model should provide a framework to assess AI reliability, reproducibility, and interpretability and help set standards for AI acceptance in critical healthcare settings.

⚠️ Why it matters? Without regulatory approval, AI models cannot be legally adopted in clinical workflows.

In medical AI, trust is everything. Achieving the right amounts of determinism and interpretability isn’t just about improving technology.

In the end it’s also about ensuring that AI-powered flow cytometry can be safely and effectively used in real-world healthcare settings.

Note from FlowView:

We understand that trust in AI is just as critical as its performance.

Our team focuses on developing AI models that strike a balance between accuracy and interpretability. This approach helps doctors and researchers receive transparent and reliable insights.

If you wish to explore how do we address these challenges, see our algorithm in action by booking a free demo. We will show you and explain to you how does it work in real-life practice.