Reviews Vol. 4 No. 1 (2024): Jan-Mar Open access
Application of Artificial Intelligence in Paramedic Education: Current Scenario and Future Perspective: A Narrative Review
- Saudi Red Crescent Authority, Riyadh, Saudi Arabia
- Disaster Management Unit, King Saud University Medical City, Riyadh, KSA
- Published
- December 25, 2023
- Pages
- 310-317
- Licence
- CC BY 4.0
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Abstract
Background:
Artificial intelligence (AI) has the potential to revolutionise paramedic education. As well as allowing for personalised learning experiences tailored to individual needs and learning styles, it can provide simulations, intelligent tutoring systems, automated grading and assessment, and predictive analytics.
Objective:
To investigate the role of artificial intelligence in transforming the landscape of paramedic education and evaluate its potential to improve learning outcomes.
Methods:
This review presented the role of AI in paramedic education and its perspective over the past twenty years. It included high-quality data and comprehensive investigations of articles available in renowned databases.
Results:
AI-based training and simulation technologies, such as virtual patients, surgical simulators, and intelligent tutoring systems,are increasingly being used in paramedic education. Virtual patients use computer-generated avatars to display symptoms and react to therapies, while surgical simulators use accurate anatomical models and haptic feedback devices to simulate surgical operations.
Conclusion:
AI has the potential to fundamentally alter how students learn, the kind of education they receive, and the efficiency with which healthcare is delivered. It can create immersive training environments, analyse medical data, and help students feel more competent, confident, and capable. This potential can be harnessed to enhance paramedic education and improve patient care outcomes.
Keywords: Artificial Intelligence, Machine Learning, Paramedic Education, Deep Learning, Predictive Modelling, Simulation, Medical Imaging
Introduction
Paramedicine is a healthcare profession focused on providing emergency medical services and pre- hospital care. [1] Artificial intelligence (AI), which has increasingly been integrated into the medical field to enhance diagnosis, treatment, and patient care, can provide numerous benefits in paramedic education, such as simulation and virtual reality, diagnostic support, data analysis, and predictive analytics. Simulations and virtual reality can provide realistic scenarios for students to practice their skills in a safe and controlled environment, while diagnostic support can assist with more accurate diagnosis of patients and provide predictions and recommendations to aid in fast, informed decision-making. [2]
AI-powered education can improve knowledge retention and competency, cost and time efficiency, real-world readiness, and continuous improvement, thus addressing the skills gap in the healthcare industry. AI-powered systems can automate tasks such as grading and assessment, reduce the need for expensive physical resources and human mentors, and provide practical skills and experience through AI- based simulations before the trainee enters real healthcare settings.[7,8] AI-powered education can help bridge the skills gap by providing accessible and high-quality training programs, while AI’s ability to analyse student data and provide feedback allows educators to continuously improve their teaching methods and curriculum.
Nonetheless, ethical considerations, patient privacy, and data security should remain central, as should human expertise and clinical judgment. Future perspectives include AI in competency assessment, telemedicine education, remote mentoring, and personalised curriculum design. Challenges and ethical considerations must be addressed to ensure responsible and equitable use. [9] Integrating AI into paramedic education can transform the way paramedics are trained and equipped to handle emergencies, allowing them to enhance their skills, improve patient care, and ultimately save more lives.
The aim of this review article was to present the role of AI in paramedic education, its perspective over the past twenty years, its present scenario, and its future applications. This is essential in order to review the existing situation, analyse the benefits and obstacles of AI applications, and bridge the gap between research and practice. Educators, decision- makers, and researchers may learn more about the advancements achieved through incorporating AI into paramedic education by focusing on the present situation, assessing the efficiency of AI applications, and considering future scenarios. This study can also point out the difficulties and limitations associated with incorporating AI into paramedic education, such as technological problems, moral questions, and the requirement for suitable instruction and assistance.
Future advancements in AI-powered paramedic education can be investigated through innovative applications of natural language processing, adaptive learning systems, and real-time data analytics. It is hoped that our evaluation will encourage more
improved through AI-based training and ensuring that paramedics have the appropriate training and support to effectively use AI in their practice, the findings can guide curriculum development and policy decisions.
This review can help close the knowledge gap between research and practice by synthesising and disseminating pertinent findings. This will assist in advancing paramedic education to ensure that paramedics have the skills and knowledge required to provide excellent emergency medical care in the AI- driven healthcare environment.
Methods
This study employed a systematic approach to gather relevant literature on the application of artificial intelligence (AI) in paramedic education, focusing on both the current scenario and future perspectives. The research involved a thorough examination of articles published within the last 20 years, available in databases such as Google Scholar, PubMed, and PMC. This study utilised a systematic approach to gather relevant literature on the application of artificial intelligence (AI) in paramedic education, both in the current scenario and future perspectives.
The literature search was conducted using key index words or phrases, including Artificial Intelligence, Machine Learning, Paramedic Education, Deep Learning, Predictive Modelling, Simulation, and Medical Imaging.
Inclusion criteria: Scientific articles addressing the study objectives and written in the English language were included, with a time range of the year 2000 onwards.
Exclusion criteria: Conversely, literature that did not address the role of AI in paramedic education and literature dated before 2000 were excluded. This was done as techniques and models that are currently relevant to paramedic education may not have been adequately developed or explored before the year 2000.
Results
We utilised the PRISMA chart [10] to depict the total number of included studies, as shown in Figure 1. Additionally, Table 1 presents a summary of the articles related to artificial intelligence in paramedic education. investigation and cooperation around this developing topic by outlining potential future directions. By identifying the competences and skills that can be
(*): Records were excluded during title and abstract screening for reasons such as not being research articles, lacking relevance to the topic, or being duplicates. (**) Records were excluded after full-text review due to reasons such as methodological flaws, insufficient data, or not meeting the inclusion criteria.
Identification
Records identified from*
Databases: (n =18)
Records screened: (n =16)
Reports sought for retrieval: (n =8)
Screening
Reports assessed for eligibility: (n =6) Reports excluded:
Included
Studies included in review: (n =6)
Identification of studies via databases and registers
Records removed before screening :
Duplicate records removed (n =2)
Records excluded**: (n =8) 4 not in English, 4 not related to paramedics
Reports not retrieved (n =2)
(n =0)
11 2021 The study assessed publications on AR/MR in paramedicine education, revealing varying quality. Applications include screen-projection immersive settings, headset-based VR, and computer-based avatar worlds. Cost-effectiveness was explored, but no research covered exposure volume or frequency. Insufficient evidence exists for AR/VR superiority over traditional simulation. 2. Wheeler et al. [11]
18 2020 Paramedics often use simulation as a primary teaching method, fostering competency and clinical skills while providing real-life experiences and strategies for effective patient care. 3. Rees et al. [12]
2020 VR is a new instructional tool that has the potential to improve paramedic skill acquisition and maintenance. The authors’ research, testing, and scaling-up of this technology began with the ParaVR initiative. 4. Eggers et al. [13]
41 2020 Early AR software research on cardiac blood flow revealed discrepancies due to funding issues, simplistic renderings, and bugs. However, users actively participated in self-guided conversations. Further research is needed to improve learning and assessment outcomes, including larger samples, in- depth interviews, and evaluations. 5. Strum et al. [14]
- 2022 Machine learning algorithms could inform point-of- care treatment and more appropriate transport destinations besides emergency departments. 6. Honda et al. [15]
AR: Augmented Reality; VR: Virtual Reality; MR: Mixed Reality.
Wheeler et al [11] emphasise the use of simulation as a paramedic teaching strategy. To create a background for potential future applications, the authors carried out a scoping assessment to look at how this tool is being utilised for paramedic education. Their review found a number of issues covered in the literature, including fidelity, cost, and equipment, skill versus scenario, virtual learning, inter-professional learning, patient safety, and perception of simulation. The research concludes that simulation is a key teaching method that is frequently utilised to instruct and train paramedics, enabling
Year Description
- 2021 The artificial intelligence-based instructional system for paramedics who specialise in dementia is useful for their line of work.
recognition of AI’s constraints. AI-enabled paramedic education can improve emergency response by equipping paramedics with the latest information and skills, leading to more efficient response and improved patient outcomes. AI algorithms can analyse data from training and real- life situations to identify areas for improvement, thus enhancing training plans and curriculum creation. Standardised training and evaluation procedures can ensure uniform quality across educational institutions and regions, promoting international norms and professionalisation of paramedic staff. Furthermore, the incorporation of AI into paramedic training offers research opportunities in the areas of customised learning, adaptive learning environments, human-AI interaction, and emergency response outcomes, improving evidence-based practice in paramedicine.
Conclusion
The incorporation of AI into computer systems allows them to carry out activities that would ordinarily require human intellect. This has the potential to fundamentally alter how students learn, the kind of education they receive, and the efficiency with which healthcare is delivered as a whole. When it comes to customised learning, whereby students might receive training and materials specifically suited to their requirements, skills, and shortcomings, AI can have a significant influence. Using virtual reality (VR) and augmented reality (AR) technologies, which can create immersive training environments where students can practice clinical procedures, emergency response scenarios, and patient interactions, AI can also improve the simulation and training aspects of paramedic education. Thus, AI can help students feel more competent, confident, and capable, resulting in better patient care outcomes.
AI systems can analyse vast volumes of data to identify patterns, connections, and insights that humans might overlook. AI-driven solutions that enable clinical decision-making, data analysis, and information retrieval might also be useful to paramedic students. Nonetheless, the incorporation of AI into paramedic education is not without its difficulties and ethical dilemmas. These include the necessity for human control and responsibility, bias
[9] Zhang K, Aslan AB. AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence 2021;2:100025. [10] Page MJ, McKenzie JE, Bossuyt PM, Boutron
in AI systems, and the privacy and security of patient data.
AI has the potential to completely change how healthcare is provided and how paramedic students are taught. We can harness this potential in the future by embracing these technological developments and resolving the associated ethical issues.
| No Authors | |
|---|---|
| 1. Birtill et al | |
| [7] | |
| 2. Wheeler et | |
| al. [11] | |
| 3. Rees et al | |
| [12] | |
| 4. Eggers et al | |
| [13] | |
| 5. Strum et al | |
| [14] | |
| 6. Honda et al | |
| [15] | |
| AR: Augmented Reality; VR: | |
| The integration and use of | |
| and mixed reality (MR) in | |
| covered in a scoping review | |
| research discovered a sparse | |
| data regarding AR/MR in | |
| Recognised categories of A | |
| projection immersive enviro | |
| avatar worlds, and VR heads | |
| emphasised the need for fur | |
| and frequency of exposure | |
| lasting effects when employ | |
| instruction. | |
| p y p | g , p g |
| practical settings. | significant improvement in paramedics’ interactive |
| In Rees et al., [12] we learn about the creation of | communication abilities. Despite no discernible |
| ParaVR, a virtual reality training simulator for | difference in empathy ratings, participants felt that the |
| maintaining paramedic abilities. The four steps of its | training was useful for their everyday job. |
| development are outlined by the authors: defining | These articles offer insights into a variety of |
| needs and specifications, developing an alpha version, | paramedic training and education-related topics, with |
| developing a beta version, and management. The | the overall focus on how to build skills, make |
| ParaVR program focuses on preserving paramedics’ | decisions, and communicate by integrating AR/MR, |
| ability to conduct needle cricothyrotomy and needle | simulation methods, VR technologies, machine |
| thoracostomy, two procedures that are seldom used. | learning, and AI more effectively. The findings |
| The essay emphasises how VR technology might | underscore the need for further study to improve |
| improve the development and maintenance of | paramedic training and practice while highlighting the |
| paramedic abilities. | possible advantages and obstacles associated with |
| Eggers et al. [13] cover the difficulties faced by | these technologies. |
| paramedic education programs in providing efficient | |
| training in technical medical and emergency | IV. DISCUSSION |
| management capabilities. Traditional lecture-based | This review offers a comprehensive overview of |
| education techniques frequently do not provide the | the application of artificial intelligence (AI) and |
| opportunity for trainees to see how their decisions | related technologies in paramedic education, with a |
| affect patient outcomes, or to practice skills | specific focus on augmented reality (AR), virtual |
| automatically. The paper focuses on the necessity of | reality (VR), mixed reality (MR), simulation, and |
| using cutting-edge instructional design techniques to | machine learning. One of the reviews included in this |
| produce more complex and realistic paramedic | study found insufficient evidence to support the notion |
| training programs. The authors advise the use of | that augmented and mixed reality (AR/MR) can |
| technology, such as augmented mannequins, to | produce results comparable to or superior to |
| enhance the realism and specificity of simulations. | conventional simulations in paramedic education.[7] |
| The use of machine learning in paramedicine is | Conversely, another study examined simulation |
| examined by Strum et al. [14] In light of the huge | practices in paramedic education and concluded that |
| paramedic data repositories that are accessible in | simulation is a crucial teaching method that enhances |
| Canada, the authors highlight the potential advantages | students’ competency and clinical skills.[11] This |
| of machine learning in paramedic clinical decision- | study outlined numerous ideas presented in the |
| making. They propose that, for complicated patient | literature, providing a comprehensive understanding |
| presentations, machine learning algorithms might | of the use of simulation. |
| help to determine point-of-care therapy and the best | ParaVR, a virtual reality training simulator for |
| destinations for patient transfer. The article also | maintaining paramedic abilities, was developed |
| underlines the significance of combining paramedic | through four phases: defining needs, alpha |
| and hospital ED data in order to evaluate the efficacy | development, beta development, and project |
| of prediction models. It is suggested that paramedic | management.[12] The project, which uses 3D |
| services, data scientists, and data centres work | modelling software, the Novint Falcon haptic device, |
| together to overcome obstacles and harness the full | and the Oculus Rift head-mounted display, highlights |
| potential of machine learning in paramedicine. | the potential of VR for improving the development |
| Honda et al. [15] focus on the use of AI to create a | and maintenance of paramedic skills in rare |
| standardised instructional system for paramedics who | procedures such as needle cricothyrotomy and needle |
| specialise in treating elderly dementia patients. The | thoracostomy. Rees et al. address a critical research |
| study used a training regimen that included lectures, | gap by providing empirical evidence for the |
| seminars, and training in rescue simulation, and an AI | effectiveness of VR in paramedic training, which was |
| system was created to evaluate face-to-face | lacking in previous studies. [16] |
| interaction, including shared eye contact, reciprocal | |
| p p y gg [ ] p | y p p |
| challenges faced by paramedic programs in the USA, | Advantages of Applying AI in Paramedic Education |
| focusing on lecture-driven training approaches. It | [22]: |
| emphasises the need for instructional designers to | AI can improve learning experiences, information |
| adopt design thinking and technologically-enhanced | retention, and student engagement by tailoring the |
| mannequins for teaching recovery skills but | experience to individual needs. Simulation and VR |
| acknowledges their limitations in simulating complex | platforms can provide paramedics with realistic |
| and realistic scenarios. The drawbacks of | settings, bridging the gap between theoretical |
| conventional lecture-driven teaching strategies in | knowledge and implementation. Intelligent tutoring |
| paramedic programs were also highlighted. This is | systems can provide immediate feedback and |
| consistent with other research that has pointed out the | direction, identifying problems and providing |
| drawbacks and difficulties of lecture-based methods | personalised help. Data analysis and predictive |
| in paramedic education. [17,18] | models can help to identify patterns, trends, and areas |
| Strum et al. [14] proposed a practical framework | for development, enabling early detection of medical |
| for machine learning in paramedicine, focusing on | crises, optimisation of resource allocation, and |
| improving clinical decisions and point-of-care | enhanced emergency response. |
| interventions. They highlighted the importance of | Nonetheless, ethical concerns, technological |
| combining hospital ED and paramedic data for | challenges, and lack of human interaction are essential |
| accurate forecasting of patient outcomes, and suggest | considerations for the proper and equitable use of AI |
| that collaboration between data scientists and | in paramedic education. One potential drawback is the |
| paramedic services could remove obstacles and create | overreliance on AI, which may diminish the |
| new care models for non-emergent ailments and | significance of human-to-human interaction in the |
| patient re-routing. The authors’ suggestion for using | learning process. Balancing technological |
| machine learning in paramedicine is consistent with | advancements with human-centred values is essential |
| other research that found machine learning algorithms | to ensure that AI enhances, rather than detracts from, |
| have the potential to enhance paramedic clinical | the quality and equity of paramedic education. This |
| decision-making. However, the particular emphasis | requires careful consideration of ethical concerns, |
| on fusing hospital ED and paramedic data to create | technological challenges, and the potential limitations |
| precise prediction models constitutes a fresh strategy. | of AI-driven learning compared to human-to-human |
| [19] | interaction. Addressing algorithmic prejudice, data |
| The project by Honda et al. [15] aimed to develop | privacy, and the possibility of AI replacing human |
| a uniform training program for dementia care | teachers is crucial. AI cannot replace mentoring and |
| paramedics in Japan. Participants took part in | human connection, and some topics, like interpersonal |
| lectures, workshops, and rescue simulations. AI was | abilities and emotional intelligence, may require in- |
| then used to assess their communication abilities, and | person instruction. Integrating new systems and |
| it was found that the training had significantly | ensuring compatibility are further challenges. |
| improved their interactive communication. | Continuous adaptation, requiring research, |
| Participants expressed satisfaction with the | development and training, is necessary to keep up with |
| application of this AI-based approach in their daily | the rapid evolution of AI technology. |
| jobs. This project is unusual in that it applies AI to | Ethical guidelines should be established for AI’s |
| create a standardised teaching program for | application in paramedic education, taking into |
| paramedics specialising in dementia care: no prior | account concerns such as data privacy, algorithmic |
| research has thoroughly investigated the application | bias, and human supervision. A collaborative |
| of AI in paramedic training specifically with regard to | approach should be encouraged between paramedics, |
| dementia-related communication skills.[20,21] | educators, and AI specialists to ensure that AI |
| Overall, the studies provide new insights into the | technologies meet the unique requirements of this |
| application of AR/MR, VR, machine learning, and AI | field. Training and assistance should be provided to |
| in paramedic education and training. They expand | ensure that paramedics and educators are skilled in the |
| upon previous research by exploring innovative | deployment of AI technology, fostering a culture of |
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Article information
- Section
- Reviews
- Published
- December 25, 2023
- Copyright
- © 2023 Meshal Menahi Alshebani, Mohammed Qismi Alanazi, Abdulrahman Eidhah Alanazi, Mohammed Abdulrahman Almotlaq, Faisal Mabkhoot ALdawsari, Abdulaziz Mohammed Almeshari, Abdullah R. Nofal. Published open access under CC BY 4.0.
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