What Kind of Mistakes Does AI Make?

In recent years, AI systems have made significant strides in industries like insurance and legal, transforming workflows and decision-making. However, AI isn’t infallible. Some mistakes are inevitable, and understanding these errors is essential to ensure AI remains a reliable and effective tool. This article will explore common types of mistakes AI can make and how professionals can strike the right balance between AI precision and human expertise.

1. Noise Errors: Too Much of the Wrong Data

AI can sometimes introduce information that’s out of context with the original source text, a precision error often referred to as 'noise.' Most of the time, this noise isn’t a complete hallucination—meaning the AI hasn’t fabricated information that isn’t in the record. Instead, it may present details that are out of context from the source. For example, it might mistakenly extract family history as if it were the patient’s own medical history.

This type of noise can create initial skepticism toward AI outputs. However, with a transparent provider like DigitalOwl, these errors are easy to identify. At DigitalOwl, click-to-evidence functionality ensures every AI-generated insight is linked directly to its source, promoting transparency and trust.

This can sometimes raise concerns that the verification process might become overwhelming and add more work—an issue brought up during a recent webinar. To keep the process manageable, users are encouraged to focus on validating only data points that could influence decision-making, rather than minor details (e.g., a mild fever or common cold symptom) that are unlikely to affect outcomes. This approach ensures that critical insights are prioritized, promoting efficient and targeted verification.

Want to learn more about how to build trust with AI? Check out our recent blog article.

2. Miss Errors: Missing Critical Information

A more serious type of AI mistake is a recall error, often referred to as a ‘miss.’ This occurs when relevant information is inadvertently excluded from the AI output, leading to gaps that can overlook critical details. For example, an AI tool may fail to detect a diagnosis like heart failure buried deep within a patient’s records. Missing such crucial insights can lead to incomplete evaluations and increase the risk of poor underwriting, claims, or legal decisions.

To prevent critical misses, DigitalOwl trains its AI models to flag uncertain information. For instance, if the AI is unsure whether something is a condition or a procedure, it will surface the information. These 'noise' errors are much easier to verify and pose far less risk to decision-making than potentially overlooking critical information.

3. Hallucinations: AI Making Things Up

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AI hallucinations are a specific type of noise that occurs when the AI system fabricates information that seems accurate but is entirely false, often leading to fear and mistrust in AI. However, DigitalOwl’s technology minimizes hallucinations with a powerful trio of advanced technologies: our proprietary Generative AI, a robust entity extraction engine, and an expansive Medical Knowledge Base. Together, these systems work seamlessly to ensure the accuracy and reliability of AI outputs, significantly reducing the risk of fabricated information.

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Hallucinations are particularly problematic when professionals rely on black-box AI systems—those with opaque or complex internal workings that make it difficult to understand how outputs are generated. This lack of transparency increases the risk of relying on unverified or potentially misleading data. As such, choosing a transparent provider, like DigitalOwl, can also significantly reduce the risk of AI hallucinations.

4. Bias in AI Outputs

AI models can unintentionally reflect biases from the data they are trained on, potentially leading to unfair outcomes. For instance, if an AI system is trained primarily on data from one demographic, it may misjudge risk factors for other populations, resulting in unequal coverage or claims assessments.

To mitigate potential bias, DigitalOwl’s AI is designed to understand and accurately summarize medical information—not to make underwriting, claims, or legal decisions. By centering on information analysis rather than decision-making, DigitalOwl’s AI supports professionals with clear, context-driven insights while reducing the risk of bias in outcomes. Additionally, DigitalOwl employs robust bias mitigation strategies, conducting regular audits and utilizing diverse training data to identify and reduce potential biases. These measures ensure our AI remains fair, transparent, and effective across varied demographics and scenarios.

Progress Starts with Trust

Mistakes are a natural part of any AI system, but trust is built through transparency and accountability. AI should not be seen as infallible but as a tool that, with proper guardrails, empowers professionals to work more efficiently and accurately. At DigitalOwl, every AI-driven insight is grounded in evidence, ensuring professionals can make decisions with confidence.

By understanding where AI can go wrong—from hallucinations to missed details—companies can develop the right checks and balances to prevent errors and foster trust. This balanced approach, combining AI precision and human oversight, allows for smarter decision-making and ensures AI serves as an enabler of progress.

Want to learn more about how you can evaluate the reliability of AI for medical record analysis? Download our recent white paper here.

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