Navigating the Regulatory Landscape: When Large Language Models (LLMs) Qualify as Medical Devices

Navigating the Regulatory Landscape: When Large Language Models (LLMs) Qualify as Medical Devices

Navigating the Regulatory Landscape: When Large Language Models (LLMs) Qualify as Medical Devices

LLMs qualify as medical devices when intended for medical purposes, but their regulation is complex due to their unique characteristics. Developers must navigate stringent safety, privacy, and efficacy requirements to achieve compliance.

I. Introduction: The Intersection of LLMs and Healthcare Regulation

Large Language Models (LLMs), exemplified by technologies such as ChatGPT and Bard, represent a profoundly transformative force with extensive potential applications across diverse healthcare domains. These advanced computational models possess an inherent ability to mimic human conversation and process immense volumes of textual and other forms of data, positioning them as exceptionally powerful tools for enhancing operational efficiency and significantly improving patient care. 

The scope of their potential utility is broad, encompassing functions from streamlining intricate clinical documentation and providing robust diagnostic support to assisting in complex treatment planning, facilitating nuanced patient communication, and accelerating the pace of medical research and discovery.

The rapid evolution and accelerating adoption of LLMs within healthcare settings necessitate a clear and precise understanding of the conditions under which these sophisticated technologies fall under the stringent purview of medical device regulations. Accurate classification is not merely a bureaucratic formality; it is paramount to ensuring patient safety and the integrity of healthcare systems. Misclassification can lead to severe and far-reaching consequences, including significant legal liabilities for developers and providers, grave risks to patient safety due to unvalidated functionalities, and substantial barriers to market access for innovative solutions.

Regulatory compliance, therefore, serves as a critical safeguard, ensuring that these innovative tools are acceptably safe, perform effectively, and meet rigorous standards for their intended medical uses. Acknowledging this burgeoning landscape, regulatory bodies, most notably the Medicines and Healthcare products Regulatory Agency (MHRA) in the UK, are actively engaged in the continuous development and refinement of guidance specifically tailored to address the unique complexities introduced by Artificial Intelligence (AI) and LLMs into the established medical device regulatory framework.

II. Foundations of Medical Device and Software as a Medical Device (SaMD) Definitions

General Definition of a Medical Device (UK MHRA Perspective)

The Medicines and Healthcare products Regulatory Agency (MHRA), operating as an executive agency of the Department of Health and Social Care in the UK, bears the crucial responsibility of ensuring that all medicines and medical devices available within the UK market are both effective and acceptably safe. The MHRA's definition of a medical device is notably broad and comprehensive. It encompasses "any instrument, apparatus, appliance, material, software or other article" that is intended for use on a patient for a defined set of medical purposes.

The core of this definition hinges upon the "purpose" for which the article is intended for human use. These specified medical purposes are delineated as:

• Diagnosis, prevention, monitoring, treatment, or alleviation of disease.

  
  

• Diagnosis, monitoring, treatment, alleviation of, or compensation for, an injury or disability.

  
  

• Investigation, replacement, or modification of the anatomy or of a physiological process.

  
  

• Control of conception.

  
  

A critical distinguishing factor within this definition is that a medical device "does not achieve its principal intended action in or on the human body by pharmacological, immunological, or metabolic means," although its function may be assisted by such means. This criterion differentiates medical devices from medicinal products. Examples provided by the MHRA and NHS England illustrate the breadth of this definition, ranging from traditional physical devices such as X-ray machines, Magnetic Resonance Imaging (MRI) scanners, and surgical instruments to increasingly prevalent digital tools, including standalone software designed for diagnostic purposes and applications (apps) on mobile devices used for patient monitoring.

Defining Software as a Medical Device (SaMD): Key Characteristics and International Consensus

The widespread availability and increasing sophistication of digital tools have profoundly transformed modern medicine, with Software as a Medical Device (SaMD) emerging as a central component of this paradigm shift. SaMD is specifically defined as "software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device". This definition is widely recognised and adopted by leading regulatory bodies globally, including the U.S. Food and Drug Administration (FDA) and the MHRA, and originates from the International Medical Device Regulators Forum (IMDRF).

A key characteristic distinguishing SaMD is its inherent independence; it possesses the capability to function on general-purpose computing platforms, such as smartphones, tablets, or personal computers, without necessitating a specific, purpose-built medical hardware setup. This independence affords significant flexibility, enabling more rapid updates and broader accessibility compared to hardware-dependent devices.10SaMD plays a crucial role in enhancing clinical outcomes through its capacity for continuous and remote monitoring, facilitating the early detection of health anomalies and enabling prompt clinical intervention.

Furthermore, it significantly increases the accessibility of medical care by extending the reach of telemedicine and virtual consultations, and fosters personalised medicine through the systematic collection and analysis of large volumes of patient-specific data. Illustrative examples of SaMD encompass diagnostic imaging software (e.g., for analysing MRI or X-ray images), Computer-Aided Detection (CAD) software used for identifying tumours or breast cancer, monitoring software designed for chronic conditions (e.g., diabetes, hypertension), and therapeutic software that either directly controls medical devices or guides clinical decision-making through digital therapeutics.

The following table summarises the key criteria for classifying software, including LLMs, as a medical device:

Table 1: Key Criteria for Medical Device Classification of Software (SaMD)

Criterion Category	Specific Criterion / Characteristic	Description / Explanation	Source(s)
Definition Source	MHRA (UK)	Defines medical device broadly to include software.	8
	IMDRF (International)	Provides the widely adopted definition of SaMD.	6
Primary Intended Purposes	Diagnosis	Identifying the nature of a disease or condition.	8
	Prevention	Averting the onset of a disease or condition.	8
	Monitoring	Observing and recording the state of a patient or condition.	8
	Treatment / Alleviation	Managing or reducing the severity of a disease or injury.	8
	Investigation / Modification of Anatomy/Physiology	Exploring or altering bodily structures or processes.	8
	Control of Conception	Devices used for family planning.	8
Key Characteristic	Operates Independently of Specific Hardware	Functions on general-purpose platforms (e.g., smartphones, PCs) without requiring a dedicated medical hardware component.	6
	Performs a Medical Purpose	The software's primary function directly aligns with one or more of the specified medical purposes.	9
Key Exclusion	Principal Action Not Pharmacological, Immunological, or Metabolic	The main effect of the device is not achieved through chemical, biological, or metabolic means within or on the human body.	8
Examples (Digital/Software)	Standalone software for diagnosis	Software designed to provide diagnostic outputs.	8
	Apps to manage medical conditions	Applications for patient self-management or remote monitoring.	9
	Symptom checkers offering medical advice	Software that provides medical advice based on user input.	9
	Online digital tools to assist in diagnosis	Cloud-based software identifying conditions from images.	9
	AI for image analysis	AI tools supporting diagnostic or therapeutic decisions through image analysis.	10

The table above is valuable because it distills complex regulatory definitions into clear, actionable criteria. It serves as a quick reference for any stakeholder to perform an initial assessment of whether their software, including an LLM, might fall under medical device regulations. By explicitly listing the "intended purposes" and "key characteristics" alongside "exclusions," it directly addresses the fundamental "what" and "how" of classification, which is a prerequisite to understanding the "when" for LLMs.

III. The Pivotal Role of Intended Purpose in LLM Classification

How "Intended Purpose" Dictates Medical Device Status for Software

The "intended purpose" stands as the singular most critical determinant in classifying any software, including a Large Language Model, as a medical device.1 This concept describes precisely what the device's functionality is designed to achieve, meticulously specifying its inputs, anticipated outputs, required user actions, and its precise integration within a broader clinical workflow.6 The MHRA explicitly underscores that an inadequately or vaguely defined intended purpose constitutes a potential serious failure to meet key medical device requirements, posing significant risks to safe and proper device use.

MHRA's Guidance on Crafting a Clear Intended Purpose

Manufacturers bear the responsibility of defining the intended purpose with an appropriate level of specificity, employing clear, clinically focused language that resonates with the indicated workflow and environment. The MHRA's guidance delineates several key elements that must be meticulously defined:

• Structure and Function: A detailed description of what the device's functionality aims to achieve, including specified inputs, outputs, user actions, and its role within the medical condition or situation in the wider clinical pathway.

  
  

• Intended Population: The specific patient population within the scope of the intended purpose, including reasonable indications and contraindications for use. If not fully stipulated, the widest possible population will be assumed, necessitating evidence of safety and effectiveness across this broad demographic.

  
  

• Intended User: The specific individuals or groups designed to use the device, detailing their roles, responsibilities, necessary qualifications, training, and experience for safe operation. For SaMDs with diverse potential users, distinguishing between primary and secondary users is crucial.

  
  

• Intended Use Environment: For SaMD, this encompasses both the physical and virtual environments. Manufacturers must provide adequate detail on the operating environment, considering interoperability, resource requirements, and critical functionalities.

  
  

The MHRA advocates for a logical, cyclical approach to defining the intended purpose, initiating this process in the early design phase and revisiting it at key junctures throughout the product development lifecycle, including the post-market phase. New evidence and insights gleaned from real-world performance should continuously inform and refine the intended purpose. Furthermore, the MHRA encourages manufacturers to make their clear intended purpose publicly available. This transparency can significantly streamline agreements with distributors, contribute to essential clinical safety documentation (particularly for NHS health IT systems), aid engagement with guidance from bodies like NICE, and foster stronger partnerships with health and social care providers.

Common Regulatory Pitfalls: Vague Purposes, Multi-Purpose Devices, and "Function Creep"

The MHRA has identified several common pitfalls manufacturers encounter when defining the intended purpose for SaMD, which are particularly pertinent to LLMs:

• Vague Intended Purposes: This issue arises when manufacturers fail to provide appropriate levels of specificity in their definition. Such vagueness makes it exceedingly difficult to generate robust evidence and conduct relevant clinical trials with clearly defined outcomes. The MHRA consistently prefers specificity, allowing for additional indications or expansions to the purpose to be added later as supporting evidence accumulates. Examples of vagueness include failing to specify reasonable indications or contraindications, neglecting to stipulate how the SaMD's output influences clinical decision-making within a pathway, or indicating multiple intended users without differentiating usage or roles. For instance, an AI-enabled medical device for diabetic retinopathy detection must precisely specify the exact patient cohort (e.g., Type 2 diabetes, ages 40-70), contraindicate others (e.g., Type 1 diabetes), and detail the intended users (e.g., trained optometrists) and the specific environment (e.g., specific scanner models and operating systems).

  
  

• Multi-Purpose Devices: This problem emerges in SaMD products when the design incorporates several functional modules, each serving a distinct and unrelated medical purpose. While each module might have a sufficiently specific intended purpose individually, their collation lacks a sensible overarching intended purpose that could be assessed via a single clinical trial. This design approach, though potentially driven by technical or commercial logic, creates a significantly more complex clinical evaluation process, as the clinical evidence and risk/benefit calculation must comprehensively cover all modules.

  
  

• Function Creep: Given the relative ease with which SaMD products can be iteratively updated, "function creep" poses a substantial challenge to the appropriateness of the intended purpose over time. This occurs when additional functionality is added to a product, causing the original intended purpose to become vague or the evidence base to become mismatched. All updates must ensure compatibility and consistency with the original intended purpose, and any additional functionality must be rigorously supported by further evidence and risk assessments. This risk is particularly pronounced for software products that initially do not qualify as SaMD but later, through added functionality or claims, fall within the scope of medical device regulations.

  
  

A critical understanding in this area is the distinction between functional intent and stated intent. While "intended purpose" is the legal determinant for medical device classification, the actual capabilities of an LLM and the claims made by its developer are the practical triggers. This implies a proactive responsibility for developers to clearly define and control their LLM's purpose. If an LLM's inherent capabilities lend themselves to medical applications—such as summarizing clinical notes or suggesting actions—even if not explicitly "intended" by the developer initially, its potential for medical use, and thus the developer's implicit claim or foreseeable use, can push it into medical device territory.

The MHRA's guidance on ambient scribing products, for instance, explicitly states that using generative AI for summarisation is likely to qualify as a medical device, whereas simple text transcription is not. This demonstrates that regulators look beyond mere stated intent to the actual or potential use based on the technology's capabilities. Developers, therefore, cannot simply claim a "general purpose" if the LLM's functionalities inherently lead to medical applications; this requires careful consideration of potential misuse or "function creep" from the outset.

Furthermore, the dynamic and user-driven nature of LLMs significantly exacerbates the challenge of "function creep," imposing a proactive regulatory burden. Unlike traditional software with fixed functionalities, LLMs can generate novel outputs or be prompted by users into medical uses not explicitly designed or intended by the manufacturer. This creates a substantial regulatory blind spot and risk, as the device's "intended purpose" can effectively evolve dynamically in the field, challenging traditional pre-market assessment models. This necessitates a highly adaptive and continuous regulatory approach, moving beyond static pre-market approvals to ongoing lifecycle management, a shift reflected in regulatory concepts such as Predetermined Change Control Plans (PCCPs) and continuous post-market monitoring. For developers, this means building in robust monitoring, version control, and mechanisms for continuous evidence generation and risk assessment throughout the LLM's operational lifecycle, rather than solely at initial market entry.

When LLMs Cross the Threshold: Specific Scenarios for Medical Device Qualification

IV. When LLMs Cross the Threshold: Specific Scenarios for Medical Device Qualification

MHRA's Direct Guidance on LLMs: Distinguishing General-Purpose from Medical-Purpose LLMs

The MHRA has provided direct guidance on the classification of Large Language Models, drawing a clear distinction based on their intended application and the claims made by their developers. LLMs that are "only directed toward general purposes and whose developers make no claim that the software can be used for a medical purpose are unlikely to qualify as medical devices". This category typically includes LLMs used for general information retrieval, creative writing, or non-medical administrative tasks.

Conversely, LLMs that are "developed for, or adapted, modified or directed toward specifically medical purposes are likely to qualify as medical devices".1 This classification also applies unequivocally if a developer "makes claims that their LLM can be used for a medical purpose," irrespective of its underlying general-purpose capabilities.1 This emphasis on both explicit development intent and implicit claims through marketing or functionality is crucial for regulatory determination.

Examples of LLM Functionalities That Likely Qualify as Medical Devices

When an LLM's capabilities extend to directly influencing patient care or clinical decision-making, it typically crosses the threshold into medical device territory. Specific functionalities that are highly likely to trigger medical device classification include:

• Diagnostic Support: LLMs that analyse medical data, such as images (e.g., dermatoscope images for melanoma identification), laboratory test results, or other clinical information, to detect diseases, identify patterns difficult for humans to discern, or aid in generating a diagnosis.

  
  

• Treatment Guidance/Prescriptive Functions: Applications that advise on specific treatment parameters, such as insulin dosage based on patient input, or those that guide clinical decision-making through digital therapeutics designed to deliver precise interventions.

  
  

• Remote Monitoring and Management: Patient-facing applications that enable self-management or continuous remote monitoring of chronic medical conditions like diabetes or depression. This also includes LLMs that analyze data from wearable sensors or other real-time inputs to detect early signs of disease or predict future medical events.

  
  

• Clinical Decision Support Systems (CDSS): LLMs integrated into CDSS that provide evidence-based actions, drug interaction alerts, diagnostic assistance, or suggest treatments aligned with clinical guidelines based on patient-specific data.

  
  

• Triage and Risk Stratification: Products that process patient information to triage individuals, stratify their risk of adverse events, or predict the likelihood of future medical events.

  
  

• Generative AI for Clinical Documentation (beyond simple transcription): While simple text transcription of speech interactions is generally not considered a medical device, the use of generative AI for more advanced documentation functions is highly likely to qualify. These include:

  
  

  • Generating summaries based on text transcripts of clinical encounters, especially when structured according to templates.

    
    

  • Formatting outputs into medical letters, discharge summaries, or other structured clinical documentation.

    
    

  • Extracting and linking terms from unstructured text to clinical codes (e.g., SNOMED CT).

    
    

  • Populating information directly into electronic health records (EHRs).

    
    

  • Suggesting actions, scheduling follow-ups, or referrals based on the clinical context derived from patient interactions.

    
    

Examples of LLM Functionalities Unlikely to Qualify as Medical Devices

Conversely, LLMs performing functions that do not directly contribute to medical diagnosis, treatment, monitoring, or alleviation of disease are generally not considered medical devices. These typically include:

• General Administrative Tasks: LLMs used for non-clinical administrative functions, such as scheduling appointments, managing general patient queries (e.g., hospital visiting hours, billing information), or providing general facility information.

  
  

• Simple Text Transcription: As noted, basic transcription of speech interactions into text, without any generative summarisation, analysis, or decision-support features, is unlikely to be classified as a medical device.

  
  

• General Information Retrieval/Educational Purposes: LLMs used to provide general medical information, educational content, or research summaries that do not offer specific medical advice, interpret individual patient data, or directly influence clinical decisions for a specific patient.

  
  

The following table provides a practical overview of LLM functionalities and their likely medical device status:

Table 2: Examples of LLM Functions and Their Likely Medical Device Status

LLM Functionality	Likely Medical Device Status	Rationale / Why	Source(s)
Simple text transcription of voice notes (e.g., doctor-patient conversation)	Unlikely	Does not interpret, analyze, or directly influence medical decisions; purely converts speech to text.	3
Summarizing clinical notes using Generative AI (e.g., creating a patient summary from a transcript)	Likely	Informs or drives medical decisions by synthesizing clinical information; goes beyond simple transcription.	3
Suggesting differential diagnoses based on patient symptoms entered by a clinician	Likely	Directly aids in diagnosis, a core medical purpose.	4
Advising on insulin dosage based on a diabetic patient's blood glucose level and dietary input	Likely	Provides specific treatment guidance, directly influencing patient therapy.	9
General patient information chatbot (e.g., answering questions about hospital visiting hours, general health tips)	Unlikely	Does not perform a specific medical purpose related to diagnosis, treatment, or monitoring of an individual patient's condition.	1
Extracting and linking terms from patient records to clinical codes (e.g., SNOMED CT)	Likely	Structures and interprets clinical data for medical purposes, influencing record-keeping and potentially billing or research.	3
Populating information directly into Electronic Health Records (EHRs) based on clinical conversation	Likely	Directly impacts the official medical record, which is used for diagnosis, treatment, and monitoring.	3
Predicting patient risk of future medical events (e.g., readmission, disease progression) based on historical data	Likely	Performs predictive analytics for risk stratification, informing clinical management.	4
Generating medical letters or other structured documentation (e.g., referral letters, discharge summaries)	Likely	Creates official medical documents used for patient care and communication between healthcare professionals.	3
Providing drug interaction alerts or suggesting evidence-based actions to clinicians (Clinical Decision Support)	Likely	Offers direct decision support for medical professionals, impacting patient safety and treatment.	4

The table above is valuable because it provides concrete examples of LLM functionalities, directly addressing the user's query about "when" an LLM becomes a medical device. It clarifies the distinction between general-purpose and medical-purpose applications, helping developers and healthcare providers assess their own LLM tools against regulatory expectations.

A significant aspect to understand here is the gradient of medical intent and the fuzzy edge of classification. Medical device classification for LLMs is not a simple binary "on/off" switch; rather, it exists on a spectrum. The "fuzziness" typically occurs at the boundary where a seemingly general-purpose capability, such as summarisation or information retrieval, begins to "inform or drive medical decisions".

The critical factor becomes the degree of influence the LLM's output has on clinical judgment or direct patient care. This means developers must conduct thorough and continuous "intended purpose" assessments, not confined to the initial design phase. They need to meticulously analyze not only what their LLM can technically do, but also how it will be used in practice and what claims are implicitly or explicitly made about its utility. This requires a deep understanding of real-world clinical workflows and potential user behaviors, especially given the generative nature of LLMs that can produce outputs beyond explicit programming, potentially leading to unforeseen medical applications.

V. Regulatory Implications and Compliance Requirements for LLM Medical Devices

Once an LLM is classified as a medical device, it becomes subject to a comprehensive set of regulatory obligations designed to ensure its safety, effectiveness, and performance throughout its lifecycle.

Overview of Key Regulatory Bodies: MHRA (UK), FDA (US), EMA (EU)

In the United Kingdom, the primary regulatory authority for medical devices is the Medicines and Healthcare products Regulatory Agency (MHRA).8 On a global scale, other prominent regulatory bodies include the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), which, alongside individual EU member state regulators operating under the EU Medical Device Regulation (MDR), play crucial roles.

Common regulatory principles and guidance often emerge from collaborative efforts by international bodies such as the International Medical Device Regulators Forum (IMDRF).These regulatory bodies are actively engaged in adapting and refining their existing frameworks to effectively accommodate the unique characteristics and complexities of AI/ML-enabled Software as a Medical Device (SaMD), with an unwavering focus on ensuring patient safety and device efficacy.2

Medical Device Risk Classification for SaMD

Once a product is definitively established as a medical device, it undergoes a classification process based on its associated risk level. This risk classification is determined by several factors, including the device's intended purpose, the duration of its use, whether it is invasive or implantable, if it is an active device, or if it contains a medicinal substance. The risk categories typically include:

• Class I: Generally regarded as low risk.

  
  

• Class IIa: Generally regarded as medium risk.

  
  

• Class IIb: Also generally regarded as medium risk, but with higher potential for harm than Class IIa.

• Class III: Generally regarded as high risk.

  
  

For SaMD, particularly those incorporating AI, the classification can vary. For instance, AI tools designed for image analysis often fall into Class IIa under EU MDR. It is also noteworthy that under the EU AI Act, most AI-SaMD are classified as high-risk, indicating a growing regulatory emphasis on the potential impact of AI in healthcare.

Core Regulatory Obligations

Developers and manufacturers of LLM-based medical devices face several stringent obligations:

• Clinical Evidence Requirements (Safety, Effectiveness, Performance): A fundamental requirement of medical device regulation is the need for robust and appropriate clinical evidence. This evidence must unequivocally demonstrate that the device performs as intended under normal conditions of use and is acceptably safe. This necessitates rigorous testing to confirm adherence to predefined performance standards, ensure accurate results, and evaluate the device's generalisability across diverse patient populations and clinical environments, while also actively identifying and mitigating biases in the algorithms.

  
  

• Quality Management Systems (QMS) and Relevant Standards: Developers are mandated to establish and maintain a comprehensive quality management system (QMS) that covers all stages of the device's lifecycle, from design and development through to post-market activities. Compliance with internationally recognized standards, such as BS EN 62304 for medical device software lifecycle processes, is essential to demonstrate adherence to safety and performance requirements.

  
  

• Registration and Conformity Assessment (UKCA/CE Marking): Any LLM-based product deemed a medical device must be formally registered with the MHRA before it can be placed on the UK market.3 For clinical use within the National Health Service (NHS) in the UK, a UK Conformity Assessed (UKCA) certificate is required. However, a valid CE mark remains acceptable until June 30, 2028, providing a transition period for manufacturers.

  
  

• Challenges with Software of Unknown Provenance (SOUP) in LLMs: A significant hurdle for developers integrating LLMs, particularly open-source models, is the concept of Software of Unknown Provenance (SOUP). Many open-source LLMs are likely to be considered SOUP if they are adapted or used as a component within a broader medical device by a third party.1 Developing medical devices that incorporate SOUP components, while adhering to a stringent QMS and demonstrating compliance with standards like BS EN 62304, can prove "troublesome." This difficulty stems primarily from the potential lack of necessary documentation for the open-source LLM itself, or the inaccessibility of such documentation to the developer of the overarching medical device.1 It is crucial to emphasize that, despite these inherent difficulties, LLM-based medical devices are unequivocally 

  not exempt from the rigorous safety and effectiveness requirements mandated by medical device regulations.

  
  

A significant structural impediment arises from the unseen burden of SOUP on open-source LLM adoption. While open-source LLMs offer considerable flexibility and potential cost advantages, their designation as SOUP creates substantial practical barriers to their integration into regulated medical devices. The absence of a compliant development history and the formal documentation required by medical device standards make it exceedingly difficult to achieve regulatory compliance. This "troublesome" aspect translates into higher development costs, extended timelines, and increased regulatory risk for manufacturers.

This situation could inadvertently create a dichotomy, where innovative open-source LLMs struggle to penetrate the regulated healthcare market, while proprietary models with meticulously controlled and documented development processes gain a significant competitive advantage. This highlights a pressing need for either regulatory adaptation specifically for open-source components or the development of industry-wide best practices for documenting and validating open-source AI models intended for medical use.

Post-Market Surveillance and Vigilance Reporting

Regulatory obligations extend beyond pre-market approval to encompass robust post-market surveillance. Manufacturers are required to establish comprehensive mechanisms for continuously monitoring the device's performance once it is on the market, identifying any adverse events, and implementing corrective actions as necessary. Specific vigilance reporting guidelines delineate the types of adverse events that may cause harm and necessitate reporting, along with the established procedures for notifying the MHRA. In England and Wales, for example, this typically involves the use of the Yellow Card Scheme. Continuous post-market monitoring is particularly crucial for AI models, including LLMs, to detect any performance degradation, the emergence of biases, or unexpected risks that may manifest after deployment in real-world clinical settings.

This emphasis on continuous monitoring represents a fundamental shift from static approval to continuous lifecycle management. Traditional medical device regulation often focused on a one-time pre-market approval based on a fixed design. However, AI/ML, including LLMs, are characterised by their capacity for "continuous learning" and "adaptive learning," meaning they can evolve and adapt over time, even post-market. Regulatory bodies are actively responding to this dynamic nature by developing and implementing concepts such as "Predetermined Change Control Plans" (PCCPs) and emphasising robust, continuous post-market surveillance.

This signifies a philosophical shift towards an ongoing, lifecycle-based management approach, where the iterative nature and data dependency of AI/ML devices necessitate continuous oversight to ensure their sustained safety and effectiveness as models adapt or encounter new data. For LLM developers, this means that regulatory compliance is not a singular event but an ongoing commitment requiring robust internal processes for continuous monitoring, meticulous validation of updates, regular risk re-assessment, and transparent reporting. This also implies a greater reliance on real-world evidence and performance data collected after market entry.

The following table summarises the key regulatory requirements for LLM medical devices, with a focus on the UK context:

Table 3: Key Regulatory Requirements for LLM Medical Devices (MHRA/UK Focus)

Requirement Category	Specific Requirement	Description / Key Action	Relevance / Challenge for LLMs	Source(s)
Foundational	Intended Purpose Definition	Precisely define the medical purpose, target population, users, and environment. Must be specific and clear.	Critical for classification; vagueness can lead to regulatory issues and mismatched evidence.	1
	Risk Management System	Implement a systematic process for identifying, evaluating, controlling, and monitoring risks throughout the device lifecycle.	AI biases, misinterpretation of outputs, cybersecurity vulnerabilities are specific risks.	7
Pre-Market	Quality Management System (QMS)	Establish and maintain a comprehensive QMS (e.g., ISO 13485, BS EN 62304) covering design, development, production, and post-market activities.	Challenges with Software of Unknown Provenance (SOUP) for open-source LLMs due to lack of documentation.	1
	Clinical Evidence	Generate robust evidence demonstrating safety, effectiveness, and performance as intended under normal conditions of use.	Must address generalizability across diverse populations and actively mitigate biases in the AI model.	1
	Technical Documentation	Compile a detailed technical file supporting conformity assessment, including design, manufacturing, risk analysis, and clinical evaluation.	Requires thorough documentation of model architecture, training data, and development process.	5
	Conformity Assessment & Registration	Obtain UKCA marking (or CE mark until June 2028) and register the device with the MHRA.	Essential for legal market access in the UK.	3
Post-Market	Post-Market Surveillance (PMS)	Establish mechanisms for continuous monitoring of device performance, adverse events, and corrective actions after market entry.	Crucial for detecting performance degradation, emergent biases, and unexpected risks in adaptive AI models.	5
	Vigilance Reporting	Report adverse incidents and safety corrective actions to the MHRA (e.g., via Yellow Card Scheme).	Specific guidelines outline reporting requirements for software-related issues.	5
	Change Management	Implement processes for managing updates and changes to the LLM, potentially using Predetermined Change Control Plans (PCCPs).	Addresses the adaptive and continuously learning nature of AI/ML; ensures continued safety and effectiveness post-update.	4

The table above is valuable as it summarizes the complex regulatory requirements for LLM medical devices into a digestible format. It provides a clear checklist of essential steps for market entry and ongoing compliance in the UK. By highlighting the specific relevance and challenges for LLMs within each requirement, it offers practical insights for developers and compliance officers navigating this evolving landscape.

Addressing Unique Challenges of AI/ML and LLMs in Medical Device Regulation

VI. Addressing Unique Challenges of AI/ML and LLMs in Medical Device Regulation

The integration of Artificial Intelligence and Large Language Models into medical devices introduces a distinct set of challenges that necessitate specialized regulatory approaches.

Good Machine Learning Practice (GMLP) Principles

To address the iterative nature and data dependency inherent in AI and Machine Learning (ML), the International Medical Device Regulators Forum (IMDRF) has established ten guiding principles for Good Machine Learning Practice (GMLP), which are increasingly adopted by national regulators like the MHRA and FDA.7 These principles are designed to ensure that AI-powered medical devices remain safe, effective, and clinically relevant throughout their entire lifecycle. Key among these are:

• Clearly Defined Intended Use: AI models must have a meticulously documented intended use that aligns precisely with regulatory requirements.

  
  

• Robust Software Engineering & Cybersecurity: Strong security protocols, comprehensive risk management, and rigorous software quality assurance practices are deemed essential to protect both patient data and device integrity.

  
  

• Representative & Bias-Free Clinical Data: AI models must be trained on diverse, high-quality datasets to prevent the introduction or perpetuation of biases and to ensure reliable real-world performance across varied patient populations.

  
  

• Human-AI Interaction Considerations: The design must ensure that AI serves to assist, rather than replace, healthcare professionals, with clear communication of the system's capabilities and limitations to users.

  
  

• Continuous Post-Market Monitoring: AI models require ongoing surveillance after deployment to detect performance degradation, identify emergent biases, and manage unexpected risks.

  
  

Managing Bias and Ensuring Data Quality, Diversity, and Representativeness

The potential for bias within AI models, particularly LLMs, is a significant concern. Training data that is limited, non-representative, or inherently biased can perpetuate or even exacerbate existing healthcare disparities, leading to inaccurate or unsafe outcomes for certain patient groups. Regulatory bodies like the FDA place a strong emphasis on bias control, advocating for strategies to identify and address bias throughout the total product lifecycle (TPLC) of AI-enabled devices, ensuring that devices benefit all relevant demographic groups equitably.

NHS guidance for AI-enabled ambient scribing products explicitly highlights the high potential for bias in AI due to training data limitations, noting, for example, varying success with different accents and dialects. Therefore, ensuring high-quality, diverse, and representative data for training, testing, and validation is paramount.

Transparency, Explainability, and Interpretability of LLM Outputs

A key challenge specific to AI, and particularly pronounced with complex LLMs, involves concerns related to AI explainability, interpretability, and overall transparency.5 The generative and often opaque nature of LLMs can make their decision-making processes difficult to understand, earning them the moniker "black box" models. Regulators, including the FDA, consider transparency essential, recommending that key information about AI functionalities is accessible and understandable to users.The EMA has also developed "Large language model guiding principles" for its staff, promoting safe and responsible use while acknowledging challenges such as the potential for irrelevant or inaccurate responses from LLMs.

The persistent push for explainability in AI, despite the technical complexity, reflects a strategic regulatory approach to the challenge of "black box" AI. Regulators are clearly pushing for greater Explainable AI (XAI) to ensure that clinical users can understand why an LLM produced a particular output. This understanding is critical for building trust, establishing clear lines of liability, and ensuring safe and effective clinical practice. This emphasis will likely drive a demand for LLM architectures and development practices that prioritize interpretability, even if this comes with a trade-off in raw performance. Manufacturers will need to invest in methods to explain their LLM's outputs, potentially through structured "model cards" or other transparency mechanisms, to meet regulatory expectations and foster broader user adoption. This represents a significant technical and ethical hurdle for the widespread deployment of complex LLMs in high-stakes medical contexts.

Regulatory Approaches to Continuous Learning and Adaptive AI (e.g., Predetermined Change Control Plans - PCCPs)

The ability of AI/ML SaMDs to evolve and adapt over time, often through continuous learning from new data in an unsupervised manner, presents a unique regulatory challenge. Traditional regulatory frameworks are designed for static, pre-approved devices. To accommodate this dynamic nature, regulatory bodies are evolving their frameworks to include concepts such as "predetermined change control plans" (PCCPs). PCCPs are designed to address post-market algorithm updates by detailing how an AI/ML SaMD is expected to change over time and outlining the manufacturer's plan for validating its continued safe and effective function after such changes.This approach aims to provide regulatory flexibility, allowing for innovation and iterative improvements without requiring frequent, full resubmissions.14

Cybersecurity and Data Compliance Considerations (e.g., GDPR, DSPT)

Given that LLM-based medical devices often handle sensitive patient data, robust software engineering and cybersecurity practices are paramount.7 LLMs, particularly those with generative capabilities, may introduce novel and unique cybersecurity challenges, including the potential for unintentional new functions or vulnerabilities through user-provided instructions.3 Compliance with stringent data protection regulations, such as the UK General Data Protection Regulation (GDPR) and adherence to the Data Security and Protection Toolkit (DSPT), is crucial. This includes implementing measures like end-to-end encryption, strict access controls, and ensuring transparency about information use and sharing.3 Cyber Essentials Plus certification is also often required for digital products within the NHS.3

User Training, Over-Reliance, and Liability

The safe and effective deployment of LLM medical devices heavily relies on appropriate user interaction and training. Providing comprehensive training to staff on the approved and appropriate use of ambient scribing products, for example, is critical. This training must emphasise the ongoing responsibility of practitioners to meticulously review and revise any outputs generated by the LLM, thereby mitigating risks such as output errors (inaccurate or incomplete documentation) and the dangerous phenomenon of "over reliance or automation bias" from users. The GMLP principles explicitly include human-AI interaction considerations, underscoring that AI should assist, not replace, healthcare professionals, and that users must fully comprehend the system's limitations. Furthermore, NHS organisations may retain liability for claims arising from the use of AI products, particularly concerning non-delegable duties of care. This necessitates clear and comprehensive contracting arrangements with suppliers to mitigate potential financial exposure.

The consistent emphasis on human review, comprehensive training, and the "assist, not replace" principle suggests that regulatory bodies are implicitly relying on the "human-in-the-loop" as a primary regulatory mitigation strategy for LLM-based medical devices. This approach acknowledges the inherent fallibility and potential for unexpected or erroneous outputs from generative AI. The implication is that even highly sophisticated LLM medical devices will likely require significant human oversight and validation of their outputs for the foreseeable future. This has profound implications for workflow design, staffing models, and the overall cost-effectiveness of deploying such systems, as the efficiency gains from automation must be carefully balanced against the critical need for human verification and accountability. It also underscores the paramount importance of robust user training and clear labeling regarding the AI's precise capabilities and inherent limitations.

VII. Recommendations for Stakeholders

The evolving regulatory landscape for LLMs in healthcare necessitates proactive and diligent engagement from all stakeholders.

For Developers:

• Proactive Regulatory Assessment: Conduct early and continuous assessments of your LLM's intended purpose and functional capabilities to accurately determine its medical device status. It is crucial not to rely solely on a general-purpose claim if the functionality or marketing explicitly or implicitly suggests a medical use.

  
  

• Clear and Specific Intended Purpose: Meticulously craft and rigorously maintain a precise intended purpose statement. This statement must clearly define the target patient population, the intended users, and the specific use environment. Actively work to avoid vagueness and proactively address the potential for "function creep" by carefully managing iterative updates.

  
  

• Robust Quality Management System (QMS): Implement a comprehensive QMS (e.g., aligned with ISO 13485 and BS EN 62304) from the very outset of development. This is critical for ensuring traceability, meticulous documentation, and consistent quality, even when incorporating open-source components.

  
  

• Comprehensive Clinical Evidence: Plan for and systematically gather robust clinical evidence that unequivocally demonstrates the LLM's safety, effectiveness, and consistent performance for its stated intended purpose. This must include strategies to identify and address potential biases within the training data and model outputs.

  
  

• Embrace Lifecycle Management: Develop and implement strategies for continuous monitoring, meticulous validation of updates (potentially via Predetermined Change Control Plans - PCCPs), and robust post-market surveillance. This is essential for effectively managing the adaptive and continuously learning nature of LLMs.

  
  

• Prioritise Transparency and Bias Mitigation: Design LLMs with explainability (XAI) as a core principle. Implement rigorous processes for identifying, quantifying, and mitigating biases in both the training data and the generated outputs. Consider incorporating mechanisms like "model cards" to communicate key information about the AI model to users.

  
  

• Strong Cybersecurity: Integrate robust cybersecurity measures throughout the entire development and deployment lifecycle of the LLM. This is vital to protect sensitive patient data, maintain device integrity, and guard against novel vulnerabilities introduced by generative AI.

  
  

For Healthcare Providers/Adopters:

• Due Diligence in Procurement: Conduct thorough assessments of the regulatory status (e.g., MHRA registration, UKCA/CE mark) and the supporting clinical evidence for any LLM-based product. A complete understanding of its defined intended purpose and inherent limitations is crucial before adoption.

  
  

• Mandatory User Training: Ensure that all staff who will interact with LLM products receive comprehensive and ongoing training on their appropriate and approved use. This training must strongly emphasize the critical need for users to meticulously review and revise all LLM-generated outputs, and to understand the potential risks associated with overreliance or automation bias.

  
  

• Robust Data Governance: Implement and strictly adhere to strong data compliance and security measures (e.g., UK GDPR, DSPT, end-to-end encryption, access controls) when integrating LLM products into existing IT systems and clinical workflows.

  
  

• Clear Liability Arrangements: Establish clear and comprehensive contracting arrangements with suppliers of LLM medical devices. This is essential to delineate responsibilities and mitigate potential financial exposure related to AI product use, particularly in light of non-delegable duties of care in healthcare.

  
  

• Continuous Monitoring and Feedback: Actively monitor the real-world performance of deployed LLM solutions within clinical settings. This includes identifying any emerging safety risks and providing timely feedback to both manufacturers and regulatory bodies (e.g., through the Yellow Card Scheme).

VIII. Conclusion: The Evolving Landscape of LLM Medical Devices

An LLM becomes a medical device primarily based on its intended purpose, which can be explicitly stated by the developer or, critically, inferred from its inherent functionality and any claims made regarding its medical utility. Functionalities that directly inform or drive medical decisions, such as those related to diagnosis, treatment, monitoring, advanced summarization of clinical data, clinical coding, or suggesting specific actions, are key triggers for medical device classification. This highlights that the regulatory focus extends beyond mere declarations to the actual and foreseeable use of the technology in a clinical context.

The regulatory landscape governing LLMs in healthcare is dynamic and rapidly evolving. It is progressively shifting towards a lifecycle management approach that is better equipped to address the unique challenges posed by AI. These challenges include the pervasive issue of "function creep" (where capabilities expand beyond initial intent), the critical need to manage and mitigate bias, the imperative for transparency and explainability in complex models, and the complexities introduced by the use of Software of Unknown Provenance (SOUP). While significant global harmonization efforts are underway, evidenced by initiatives like Good Machine Learning Practice (GMLP) and Predetermined Change Control Plans (PCCPs), challenges persist in achieving full alignment across diverse jurisdictions, particularly concerning risk classification systems and specific submission requirements. This means manufacturers operating internationally must navigate a complex patchwork of regulations despite common underlying principles.

Looking ahead, the MHRA and other global regulators remain committed to fostering responsible innovation while steadfastly ensuring patient safety and device efficacy.1 The emphasis will continue to be placed on the generation of robust clinical evidence, the transparent development of AI, and comprehensive, continuous post-market surveillance. Future regulatory frameworks will likely need to adapt further to the inherently dynamic and adaptive nature of LLMs, potentially through the development of more agile approval processes that judiciously balance the imperative for rapid innovation with the paramount need for patient protection. This includes finding pragmatic solutions for integrating open-source AI components into regulated environments. Ultimately, sustained and proactive collaboration among regulators, technology developers, and healthcare providers will be indispensable for successfully navigating this complex and rapidly advancing field, ensuring that the transformative potential of LLMs is harnessed safely and effectively for the benefit of patients worldwide.

Nelson Advisors > Healthcare Technology M&A

.

Nelson Advisors specialise in mergers, acquisitions & partnerships for Digital Health, HealthTech, Health IT, Consumer HealthTech, Healthcare Cybersecurity, Healthcare AI companies based in the UK, Europe and North America. www.nelsonadvisors.co.uk

Nelson Advisors regularly publish Healthcare Technology thought leadership articles covering market insights, trends, analysis & predictions @ https://www.healthcare.digital 

We share our views on the latest Healthcare Technology mergers, acquisitions & partnerships with insights, analysis and predictions in our LinkedIn Newsletter every week, subscribe today! https://lnkd.in/e5hTp_xb 

Founders for Founders > We pride ourselves on our DNA as ‘HealthTech entrepreneurs advising HealthTech entrepreneurs.’ Nelson Advisors partner with entrepreneurs, boards and investors to maximise shareholder value and investment returns. www.nelsonadvisors.co.uk

#NelsonAdvisors #HealthTech #DigitalHealth #HealthIT #Cybersecurity #HealthcareAI #ConsumerHealthTech #Mergers #Acquisitions #Partnerships #Growth #Strategy #NHS #UK #Europe #USA #VentureCapital #PrivateEquity #Founders #BuySide #SellSide

Nelson Advisors LLP

Hale House, 76-78 Portland Place, Marylebone, London, W1B 1NT

Contact Us

lloyd@nelsonadvisors.co.uk

paul@nelsonadvisors.co.uk

Meet Us

Digital Health Rewired > 18-19th March 2025 > Birmingham, UK

NHS ConfedExpo  > 11-12th June 2025 > Manchester, UK

HLTH Europe > 16-19th June 2025, Amsterdam, Netherlands

HIMSS AI in Healthcare > 10-11th July 2025, New York, USA

World Health Summit 2025 > October 12-14th 2025, Berlin, Germany

HLTH USA 2025 > October 18th-22nd 2025, Las Vegas, USA

MEDICA 2025 > November 11-14th 2025, Düsseldorf, Germany

Nelson Advisors > Leading European Healthcare Technology M&A Advisory

https://www.healthcare.digital/single-post/nelson-advisors-leading-european-healthcare-technology-mergers-acquisitions-advisory-firm