AI Writers in Healthcare: Between Hype and Reality

Research shows that AI writers can produce well-written but factually incorrect information, omit important details, or confuse speakers—errors that do not resemble human mistakes and that can influence clinical decisions (1).

At the same time, this technology is being used on a scale rarely seen before in digital healthcare systems. A growing proportion of clinics report that they are working with AI-based documentation solutions, and some healthcare systems have implemented them in large-scale programs despite limited patient safety data (2). In Sweden, this development has led the Medical Products Agency to announce inspections of manufacturers in the spring of 2026, focusing on how models are trained, how risks are managed, and how the quality of medical content is ensured.

The Swedish Medical Products Agency is reviewing AI assistants in healthcare

This increased regulatory focus can be explained by two overarching EU regulatory frameworks. First, the MDR classifies AI writing tools as medical devices when their output is used as part of the basis for medical decision-making, which entails requirements for CE marking, risk management, clinical evidence, and continuous monitoring (4). Second, the EU’s AI Regulation (AI Act) classifies many AI-powered medical decision-making systems as high-risk systems, which requires traceability, robustness, data governance, and human oversight (5). Overall, the technology is undergoing a regulatory paradigm shift in which its clinical significance is now taken much more seriously than before.

Perspective: AI Writing Tool Developers—From Smart Tools to Full-Scale Medical Devices

For manufacturers, the introduction of AI-powered documentation tools no longer involves the development of an efficient administrative support system, but rather the creation of a medical device that must withstand legal and clinical scrutiny. Research shows that while AI-powered documentation tools often reduce documentation time and improve workflows, they simultaneously introduce new error patterns that require systematic and standardized evaluation. The NPJ study points out that today’s solutions are often implemented faster than safety validation can keep up, and that there is a lack of established methods for measuring clinically relevant errors (1).

The JMIR article also shows that, to date, research in this field has focused almost exclusively on time savings, while issues related to safety, clinical reasoning, and system-wide effects have been studied to a much lesser extent (2). The large systematic review from 2025 confirms this picture by showing that the studies are small, heterogeneous, and methodologically diverse, meaning that the evidence is not yet robust enough to draw general conclusions about the technology in clinical settings (3).

Regulatory frameworks add further complexity. The MDR requires manufacturers to be responsible for postmarket surveillance, incident reporting, and quality assurance over time (4). This is particularly challenging for AI systems that are constantly evolving through updates or adjustments to their models. At the same time, the AI Act imposes requirements regarding explainability, transparency, and the management of bias, particularly since there is documentation showing that AI-powered writing tools may perform worse for patients with dialects, accents, or linguistic variations (1)(5). For manufacturers, this means that development work must include comprehensive data review, transparency in model training, and procedures for detecting and correcting deviations in system performance.

Overall, manufacturers are entering an era in which AI writers must not only be fast, but also clinically accurate, regulatory-compliant, and continuously monitored. The technology’s potential is significant, but so is the responsibility.

Perspective: Patients—Trust, Integrity, and the Future of Clinical Thinking

From the patient’s perspective, AI-powered note-takers are first and foremost a matter of trust. The systematic review shows that patients generally accept the technology if they are clearly informed about its purpose and function, but that a significant minority express concerns about privacy and data storage (3). The JMIR article highlights that some patients become more reluctant to share sensitive information when they know that a system is recording the entire conversation, which could affect the quality of the medical history (2).

An even more important perspective concerns how AI affects clinicians’ own reasoning. Documentation is not merely an administrative process—it is part of how clinicians think, analyze, and prioritize. The NPJ article warns that AI-generated notes risk disrupting this process by taking over a central part of the cognitive chain, which in some cases has already been shown to negatively affect clinical decisions (1). The YKNOT study found that approximately one in six AI-generated notes was assessed as having a negative impact on clinical decision-making (2).

Privacy and security are also key considerations. Several studies report that patients are hesitant about smartphone-based recording during visits, and that acceptance is strongly linked to how clearly the process is explained (3). The implementation of this technology must therefore include clear procedures for consent, data storage, and transparency, which are also central to the AI Act (5).

The patient perspective, therefore, is not just about whether the technology works, but about whether it strengthens or weakens the relationship between the healthcare provider and the patient, and whether the clinician retains control over their own reasoning.

The big question: Does saving time lead to better care?

AI-powered documentation tools have been introduced with a clear goal: to reduce the documentation burden and free up time for patient visits. Research shows that these systems can actually cut down on documentation time, reduce administrative stress, and, in some cases, improve workflows and clinician engagement (2)(3). However, all three primary sources point to the same conclusion: we do not yet know whether this leads to better care.

There are no robust studies showing that AI-powered writing tools improve diagnostics, reduce errors, or improve patient outcomes. It is entirely possible that the technology will do so—but at present, that kind of evidence is lacking. As the researchers behind the JMIR article put it: the next phase is not about how quickly we document, but about how the time saved affects clinical decisions and patient health (2).

AI-powered writing tools are likely to become a central part of the future of healthcare. But for them to contribute to better care—not just faster healthcare administration—robust research, clear regulation, and a focus on safety and quality at every step are required.