When people in the 1980s envisioned the future, technological advancements often took center stage in utopian scenarios—a world where humans worked hand in hand with their robotic assistants. Today, imagining a world in which humans and AI collaborate seamlessly no longer requires us to fast-forward into the future. We are already living in a reality where the synergy between human ingenuity and technological capabilities is not just possible but actively shaping our world.

But every system has its blind spots, and knowing where they lie makes all the difference: When it comes to accuracy, humans and AI each bring unique strengths and challenges to the table. By understanding these differences, we can design systems that harness the best of both worlds to deliver exceptional outcomes. To shed light on this topic, we’ve broken it down into five key categories, offering a clear perspective on the advantages and limitations of both. Let’s dive in!

1. The Double Standard in Accuracy

Is there a universal expectation for AI accuracy? Not really, but our tolerance for errors is usually tied to risk. If an AI system reminds hospital staff of routine administrative tasks, occasional mistakes may be acceptable. But if an AI is diagnosing skin cancer, the margin for error shrinks dramatically.

There’s an inherent bias in how we perceive errors: we accept human mistakes as inevitable, yet we demand near-perfection from AI. This is particularly true in high-stakes industries like MedTech, where accuracy directly impacts patient outcomes. However, no invention in human history has ever been truly “perfect” or “infallible”—because we ourselves are not. Instead of chasing an unrealistic standard, we should ask: what should our benchmark for AI accuracy be?

One approach is to first evaluate human performance as a baseline. For example, in categorizing BfArM field safety notifications based on the described issue (e.g., IMDRF Code Annex A), auditors agree in about 90% of cases. If trained experts only reach consensus at this rate, what level of accuracy should we realistically expect from an AI? By understanding the limits of human decision-making, we can set fair and practical expectations for AI—aligning our demands with what is realistically achievable rather than an illusion of perfection.

2. Unique Strengths

When it comes to AI versus humans, both sides bring unique strengths to the table. Humans excel in tasks requiring empathy, contextual understanding, and adaptability. They bring judgment, creativity, and accountability to decision-making, making them indispensable for complex, nuanced challenges. AI, on the other hand, offers unmatched consistency, scalability, and speed. It can process massive datasets without fatigue, delivering efficiency beyond human capability.

The key to effective AI integration is applying it where it truly adds value: extracting relevant data from long texts, categorizing information with consistency, and summarizing content efficiently. But how does this play out in real life? Imagine a 30-page report on a medical device incident, where crucial information—such as a fatality and its cause—is buried within the text. While AI can process the document much faster than a human, can it accurately interpret the context and recognize the death as an actual fatality? This highlights the importance of knowing when to rely on AI and when to involve human expertise. A simple checklist can help: What is the risk involved? How repetitive is the task? What is the human error rate? Answering these questions allows us to find the right balance between automation and human oversight.

3. Limitations and Weaknesses

Human accuracy is often affected by fatigue, distractions, and inconsistencies, especially in high-pressure situations. AI, on the other hand, relies entirely on the quality of its training data and struggles with tasks requiring contextual reasoning or precedent. Moreover, its decision-making processes can be difficult to interpret, making transparency and accountability more complex.

Consider real-world scenarios where AI alone reaches its limits: an AI system tasked with selecting relevant scientific publications for a medical device evaluation may produce flawed recommendations if trained on poor data—overlooking crucial papers or suggesting irrelevant ones. Users who blindly trust these outputs risk missing essential details. Similarly, AI may struggle with nuanced judgment calls, such as assessing whether a rare adverse event could plausibly be linked to a medical device. Even in process-driven tasks, like ensuring a follow-up action within an organization is completed on time, AI alone may not be reliable. These examples highlight why, despite AI’s efficiency, human oversight remains essential in critical decision-making.

4. Accountability Matters

Humans are naturally accountable for their decisions, supported by training, experience, and auditable processes. Similarly, AI must be subject to rigorous validation, continuous monitoring, and strict oversight to ensure compliance. However, a fundamental question remains unresolved in many cases: who is responsible when an AI system makes a mistake?

Certain jurisdictions, such as the European Union, are taking a clear stance on this issue. Regulations like the EU AI Act establish that AI providers must ensure high-quality, reliable systems—or face increased liability risks. As AI regulations evolve, more countries are likely to adopt similar frameworks. At the same time, users of AI-driven systems must also take responsibility by using AI as intended and not misapplying it beyond its designed scope. The future of AI accountability will likely be a shared responsibility between developers, regulators, and end users, ensuring both innovation and ethical deployment.

5. Best Practices for Accuracy

Combining the strengths of humans and AI can mitigate the weaknesses of both. For humans, regular training, collaborative reviews, and fatigue management are essential to maintain accuracy. For AI, explainable algorithms, frequent retraining with updated data, and human oversight ensure that systems remain reliable and adaptable.

Where Do We Go from Here?

In a nutshell: Humans bring judgement, empathy, and adaptability; AI excels in consistency, efficiency, and scalability. Together, they form a powerful team. However! Achieving the right balance requires a thoughtful approach:

• Set realistic expectations: AI can’t achieve perfection, and neither can humans.

• Tailor accuracy to risk: The higher the stakes, the greater the need for precision.

• Use appropriate testing: Whether through Turing tests, ground truth comparisons, or confusion matrices, accuracy assessments must align with the task.
  
  
  

Looking Ahead!

Despite the uncertainties we face today, the future of AI accuracy is filled with promise. Over the next decade, we’ll likely see clearer guidelines for testing standards, liability frameworks, and the elusive question of what constitutes "acceptable accuracy." Until then, the key lies in thoughtful, well-rounded risk-benefit analyses to determine just how much accuracy is enough for each unique application.

In MedTech and beyond, the magic happens when human expertise meets AI’s capabilities. This powerful blend not only enhances outcomes but also builds the trust needed for technology to truly thrive.

If you want to learn how Flinn’s AI-powered solutions can support your team in achieving efficient and streamlined post-market surveillance processes, let’s talk!