Point 5(d) of Annex III classifies as high-risk AI systems intended to evaluate or classifyemergency calls by natural persons or to be used to dispatch or to establish priority in the dispatching of emergency first response services, including by police, firefighters and medical aid, as well as of emergency healthcare patient triage systems. If not developed or used correctly, the use of such systems can have risks concerning very critical situations for the life and health of persons and their property. To address those risks, such systems are classified as high-risk and subjected to safeguards to ensure they work as intended and are accurate, safe and do not discriminate.
Five types of AI systems fall within the uses cases listed in point 5(d) Annex III:
AI system s intended to be used to evaluate and classify emergency calls by natural persons;
AI systems intended to be used to dispatch emergency first response services;
AI systems intended to establish priority in the dispatching of emergency first response services;
AI systems intended to be used to dispatch emergency healthcare patient triage systems;
AI system s intended to be used to be used to establish priority in the dispatching of emergency healthcare triage systems.
AI systems that fall within the use cases of point 5(d) of Annex III AI Act and that qualify as a medical device pursuant to Article 2(1) of Regulation (EU) 2017/745 (the Medical Devices Regulation) or as radio equipment pursuant to Article 2(1)(1) of the Radio Equipment Directive fall within the scope of Annex I AI Act and shall also be classified as high-risk pursuant to Article 6(1) AI Act if they meet the conditions of this provisions (See Section III above). If Article 6(1) AI Act is not applicable, AI systems within the scope of the use cases in point 5(d) are classified as high-risk pursuant to Article 6(2) and Annex III of the AI Act.
i. The meaning of ‘emergency calls’
The AI Act does not define the notion of ‘emergency calls’. Inspiration may be drawn from Article 2(31), (36) and (38) of Directive (EU) 2018/1972 (the European Electronic Communications Code)67. Those provisions define the notions of ‘call’, ‘public safety answering point’ (‘PSAP’ ), and ‘emergency communication’. As regards the latter, emergency communication is classified as ‘communication by means of interpersonal communications services between an end-user and the PSAP with the goal to request and receive emergency relief from emergency services’, which is not limited to only calls but can also includetext messaging and video. A PSAP is ‘a physical location where an emergency communication is first received under the responsibility of a public authority or a private organisation recognised by the Member State’68.
ii. The meaning of ‘emergency first response services’
The AI Act does not define the notion of ‘emergency first response services ’. Such servicesare generally a Member State competence69 and Member State legislation designates the organisations and types of services that should be considered emergency first response services. In addition , inspiration may be drawn from Article 2(39) of the European Electronic Communications Code , which defines ‘emergency service’as those services ‘recognised as such by the Member State, that provide immediate and rapid assistance in situations where there is, in particular, a direct risk to life or limb, to individual or public health or safety, to private or public property, or to the environment, in accordance with national law ’ . Point 5(d) of Annex III and Recital 59 AI Act provide as examples of such services police, firefighters and medical aid . Such services may also include ambulance services, operation centres for information management relating to, for example, disaster risk management and forecasting , and organisations providing search, rescue, and evacuations.
iii. The meaning of ‘emergency healthcare patient triage systems’
Emergency healthcare patient triage systems may qualify as medical devices in so far as they fulfil the definition of a medical device in Article 2(1) of the Medical Devices Regulation. If such systems meet the conditions in Article 6(1) AI Act, they will be classified as high-risk pursuant to that provision and Annex I AI Actand require streamlined and consistent compliance with sectoral laws (see Section III above).
Emergency healthcare patient triage systems that do not constitute medical devices shall be classified as high-risk pursuant to Article 6(2) and point 5(d) of Annex III AI Act.
iv. The meaning of ‘forecasting’ and ‘disaster monitoring’
AI systems can also be used for forecasting or predicting situations where emergency first response services will be needed in the absence of emergency calls from natural persons, e.g. systems alerting to a situation where emergency first response services are needed. Such systemswill only fall within the scope of point 5(d) of Annex III where they are specifically intended to be used to decide when or where to dispatch emergency first response services.
a) Practical examples of AI systems falling within the high-risk use case of point 5(d)
Analysing calls and prioritising interventions
- An AI system intended to be used in emergency response centres where 112 calls are classified to assign the level of urgency and to route responders, using natural language processing. Such an AI system is high-risk, since the system is intended to evaluate and classify the calls, and to establish prioritisation in the dispatching of emergency first response services, including the actual dispatching of the services.
- An AI system is used to analyse emergency calls received by the police and to prioritise interventions according to the urgency and seriousness of the situation through the extraction of relevant information, such as relevant key words. The system assesses the urgency of the situation and the response to be given. Such an AI system is as high-risk, since it is directly involved in the evaluation, prioritisation and dispatching of incoming calls made by natural persons, and the system assesses which response should be given. Supportive tools for human review
- An AI system intended to provide real-time decision support for emergency first response services call-talkers, effectively enhances human decision-making skills by acting as an assistant to the call-taker, listening for signs or signals of life-threatening emergencies in what the caller is describing. The AI system uses real-time automatic speech recognition technology, creating high-quality transcripts and during the call, the data is analysed and compared with historical data collected from previous emergency calls. Such an AI system is high-risk, since it evaluates the call made by a natural person by assisting the emergency first response services call-taker by helping to classify the call on the basis of the severity of the situation through the listening for keywords that might indicate a medical emergency.
- AI systems intended to determine the nature and severity of emergency situations in incoming calls that are put in a queue before they are picked up. The AI system’s role is to identify the emergency as life-threatening and alert the human call-taker if the waiting incoming call needs to be prioritized, while the decision to perform an automated triage or prioritise dispatch remains with the human call-taker. Such an AI system is high-risk, since it evaluates and classifies emergency calls into those that are life-threatening and those that are not. Emergency triage systems
- AI systems used in emergency departments that are intended to prioritise patients, without performing clinical assessment medical acts or diagnostic functions . Such an AI system is high-risk, since it is intended to be used to establish priority in the dispatching of emergency healthcare patient triage systems.
- AI systems intended to be used as chatbots to trigger emergency medical emergency services in medical institutions without performing clinical assessment medical acts or diagnostic functions. Rapid alert systems
- An AI system intended to be used to support decisions on allocating resources and pre-positioning assets to combat wildfires, where the system not only predicts the likelihood and the direction of wildfire evolution across an area’s protected forests, but also automatically triggers the dispatching of emergency first response services or provides clear recommendation of staff allocation in order to contain the wildfire. The system does not replace human decision-making but supports staff. Such an AI system is high-risk, since it is specifically intended to trigger emergency first response services, as well as to decide when and/or where to dispatch emergency first response services. The impact of such system is related to its decisive role for the dispatching of resources and to locations, which are decisions made in critical situations for the life and health of persons and the protection of their property. Language tools
- AI systems intended to be used as a mental health crisis triage chatbot, which uses natural language processing to assess severity and urgency of chat-based contacts with emergency mental health services without performing clinical assessment or diagnostic functions . Such an AI system is high-risk, since it directs mental health intervention responses, carries a risk of misclassification and provides a first emergency triage function.
b) Practical examples of AI systems falling outside the high-risk use case of point 5(d)
Transcribing emergency calls
- An AI system intended to transcribe poor-quality emergency calls, or calls where callers may be panicked or unable to communicate, identifying relevant keywords and transcribing them automatically, thereby saving time that the call-taker would otherwise spend asking the caller to repeat themselves. Such an AI system falls outside the use case listed in point 5(d), since it aids the emergency first response services call-taker in the evaluation of a call made by a natural person by focusing on specific keywords related to emergency first response services, but does not evaluate or classify the call. Forecasting
- AI systems that analyse various sets of data for fast indications of potential disasters before emergency first response services are made aware through emergency communications. These systems can for example identify flood-prone regions and suggest mitiga tion strategies, model potential wildfire spread patterns based on wind, temperature and vegetation density, process social media feeds to detect when a new topic is trending and monitor weather conditions and seismic activity. Such an AI system falls outside the use case listed in point 5(d), since it is not intended to decide when and/or where to dispatch emergency first response services , but rather resembles systems that are intended for the general forecasting of potential disasters, without a link to dispatching emergency first response services. Enhanced learning and training
- An AI driven simulation platform used to provide realistic training environments. The system creates among others scenario-based training for first responders, allowing them to practice responses to floods, wildfires and cyberattacks, followed by automateddebriefings and response recommendations. Such an AI system falls outside the use case listed in point 5(d), since it is intended to provide a learning environment with realistic scenarios. In practice, such systems are not used to evaluate and classify i ncoming real-time calls from natural persons , nor do they actually dispatch or prioritise emergency first response services. The experience and information gained within this environment is valuable for the development of an AI system which is to be used for the intended purposes listed in point 5(d) of Annex III of the AI Act, but is not considered high-risk for learning purposes. Identification of patients
- AI systems intended to be used to securely identify patients undergoing medical procedures, using biometric parameters. Such an AI system falls outside the use case listed in point 5(d), since the system is not intended to be use for one of the intended purposes listed therein. AI systems related to the connectivity of the service
- AI systems intended to convey traffic on the network of received emergency calls or the lack of connectivity. Such an AI system falls outside the use case listed in point 5(d), since it is not inherently responsible for triggering harmful outcomes, even if it may affect the availability of services. Estimated healing time
- AI systems intended to estimate the healing time for room-and bed-management in a hospital. Such an AI system falls outside the use case listed in point 5(d), since it is not intended to be use for one of the intended purposes lists therein. Medical appointments
- AI systems intended to schedule medical appointments. Such an AI system falls outside the use case listed in point 5(d), since it is not intended to be use for one of the intended purposes listed therein and it does not relate to life-threatening emergencies.
c) Practical examples of AI systems falling within the use case but exempted under the filter mechanism of Article 6(3)
Preparatory tools for prioritization
- An AI system intended to interpret weather risk data, such as of wildfire or flood, in advance of a probable emergency and to provide preparatory intelligence for the prioritisation of resources, such as pre-positioned firefighters or flood prevention devices, and their allocation pre-emptively. Such an AI system falls within the use case listed in point 5(d), since it is intended to be used for establishing priority in the dispatching of emergency services. However, since the system performs a preparatory task to an assessment relevant for the prioritisation that will be evaluated and confirmed by human analysts and the competent commander , it falls under the exception in Article 6(3)(d) AI Act. Supportive tools for human review
- AI systems intended to provide situational awareness of an emergency site to first responders, such as of where patients and casualties are located and what is e.g., their body temperature. This could be a system held by the on-site commander that gathers and combines the respective data from human first responders, from autonomous rescue robots or drones, and other sensor data (e.g., radioactivity or toxins). Such an AI system falls within the use case of point 5(d) of Annex III, since it is linked to emergency healthcare patient triage systems. However, since the system merely provides existing and factual information in a structured manner, without evaluating this information for the prioritisation or triage of the patients and casualties , it falls under the exceptions for AI systems intended to perform narrow procedural and preparatory tasks in Article 6(3)(a) and (d) AI Act.
- Directive (EU) 2018/1972 of the European Parliament and of the Council of 11 December 2018 establishing the European Electronic Communications Code (Recast). This Directive is currently being revised, with a proposal from the Commission expected in Q1 2026.
- Directive (EU) 2014/53/EUof the European Parliament and of the Council of 16 April 2014 on the harmonisation of the laws of the Member States relating to the making available on the market of radio equipment and repealingDirective 1999/5/EC.
- Article 196 of the Treaty on the Functioning of the European Union.