When AI gets it wrong: Why transcription errors could become healthcare and justice’s next big risk

Artificial intelligence is rapidly reshaping professional services. Hospitals are using AI-powered “scribes” to reduce physician paperwork, while law firms and courts are exploring automated transcription systems to process hearings, interviews and testimony more efficiently. The promise is compelling: faster documentation, lower administrative costs and improved productivity. Yet new findings suggest there is a significant risk hidden beneath the efficiency gains. In high-stakes environments such as medicine and law, transcription errors are not merely inconvenient for they can alter diagnoses, affect legal outcomes and create substantial compliance challenges.

A recent audit of 1,000 hours of court proceedings and medical dictations found that AI-generated transcripts contained a “critical error” in approximately 18 percent of analysed documents when compared with human-verified records. According to the analysis, these were not routine spelling mistakes. The errors included incorrect medication dosages, altered interpretations of testimony and the omission of critical qualifying words such as “not.” Such mistakes can fundamentally change meaning and potentially influence clinical decisions or legal judgments.

The hallucination problem

One of the most concerning aspects of generative AI is “hallucination”, those instances where an AI model generates information that appears plausible but is factually incorrect. In transcription systems, hallucinations occur because models attempt to predict words rather than understand context in the same way humans do.

According to Ben Walker, CEO of Ditto Transcripts, AI systems can perform impressively in controlled environments but often struggle when confronted with real-world conditions. Courtrooms and hospitals rarely provide pristine audio. Multiple speakers may talk simultaneously, regional accents can vary significantly, and technical terminology can be dense and specialized.

The audit identified three recurring problem areas, including overlapping speech leading to misattributed statements, specialised legal and medical terminology being replaced with similar-sounding common words, and the omission of negative modifiers such as “not,” creating the opposite meaning of the original statement. These findings align with broader concerns raised by healthcare and legal professionals regarding the reliability of AI-generated documentation when the consequences of error are severe.

Why healthcare is particularly vulnerable

Healthcare has emerged as one of the fastest-growing applications for AI transcription. Clinicians spend considerable time documenting patient encounters, and AI scribes can significantly reduce administrative burdens. Canadian studies have suggested that AI documentation tools may dramatically reduce physician paperwork and improve workflow efficiency. However, experts have also warned that these systems can omit critical clinical nuances and generate contextual errors when summarizing patient conversations.

This concern is especially relevant in Canada, where healthcare systems face staffing shortages and increasing administrative pressures. Many organizations view AI as a tool to improve productivity. Yet Canadian privacy experts have highlighted risks associated with AI scribes, including transcription inaccuracies, consent management, data residency concerns and protection of personal health information.

An incorrectly transcribed prescription, a missing allergy reference or an altered clinical observation could potentially affect patient care. As a result, privacy commissioners and healthcare regulators across several Canadian provinces have issued guidance requiring human oversight and review of AI-generated clinical documentation.

Legal systems face similar challenges

The legal sector faces an equally complex problem. Court transcripts serve as official records that underpin appeals, judicial reviews and case preparation. A mis transcribed testimony or omitted statement can alter the interpretation of evidence and create opportunities for legal disputes.

Unlike conventional software, generative AI systems do not simply convert speech into text. They infer language patterns and fill information gaps. While this can improve readability, it also introduces risks that are unacceptable in environments where precision is mandatory.

For this reason, many legal technology experts continue to emphasize verification by trained transcription professionals rather than fully automated workflows.

The growing use of cloud-based AI transcription services raises significant privacy and regulatory questions. Sensitive health records, legal proceedings and confidential client communications may be processed through external AI systems.

In Canada, healthcare organizations must navigate federal privacy requirements under the Personal Information Protection and Electronic Documents Act (PIPEDA) alongside provincial health privacy legislation. Experts note that organizations must carefully evaluate how AI vendors store, process and potentially reuse data.  Recent Canadian guidance regarding AI scribes has focused heavily on consent management, preventing secondary use of data for AI model training and ensuring strong governance controls before deployment.

The findings do not suggest that AI transcription should be abandoned. The technology clearly offers benefits in terms of efficiency, workflow support and administrative burden reduction. Instead, the evidence points toward a “human-in-the-loop” model.

Canadian privacy experts, healthcare organizations and legal professionals increasingly view human review as an essential safeguard rather than an optional step. AI can accelerate documentation, but humans remain responsible for validating accuracy, context and regulatory compliance.