Telemedicine, E-Health, and Multi-Agent Systems for Chronic Pain Management

Abstract

Telemedicine, telehealth, and E-health all offer significant benefits for pain management and healthcare services by fostering the physician–patient relationship in otherwise challenging circumstances. A critical component of these artificial-intelligence-based health systems is the “agent-based system”, which is rapidly evolving as a means of resolving complicated or straightforward problems. Multi-Agent Systems (MAS) are well-established modeling and problem-solving modalities that model and solve real-world problems. MAS’s core concept is to foster communication and cooperation among agents, which are broadly considered intelligent autonomous factors, to address diverse challenges. MAS are used in various telecommunications applications, including the internet, robotics, healthcare, and medicine. Furthermore, MAS and information technology are utilized to enhance patient-centered palliative care. While telemedicine, E-health, and MAS all play critical roles in managing chronic pain, the published research on their use in treating chronic pain is currently limited. This paper discusses why telemedicine, E-health, and MAS are the most critical novel technologies for providing healthcare and managing chronic pain. This review also provides context for identifying the advantages and disadvantages of each application’s features, which may serve as a useful tool for researchers.

Potential of use of modern information technology solutions in the work of hospital infection control team, including antibiotic therapy optimisation, reduction of alert pathogen infections and vaccination popularisation

“…Medical expert systems and ai-based solutions can, as it has already been demonstrated in different medical disciplines [26][27][28], significantly improve and speed up consulting cases of people suspected of infection or infectious disease and those diagnosed with an infection or infectious disease [29,30]. agent-based systems (abS) and multi-agent systems (MaS) [33] can be of help in collecting the required large amounts of data and documenting the actions taken by the epidemiologists. large language models (llMs), similar to ChatGPt [34], especially if merged with augmented reality (aR) solutions [36], can be utilised in the process of staff training in hospital infection control [38].…”

“…Developing and updating a system for preventing and combating nosocomial infections requires both deep and wide analyses of problems and finding unobvious solutions for them, which can be facilitated both by the machine learning (Ml) [83], defined as the set of computation tools and methods for searching patterns in datasets to create models [100], and large language models (llMs) [34]. Conducting internal control of actions aiming to prevent the spread of infections and infectious diseases can be efficiently supported at various stages: the collection of data can be facilitated by agent-based systems (abS) [33] and augmented reality (aR) solutions [38], data analyses can benefit from machine learning (Ml) solutions [83], while the consolidation stage of the process can be optimised by artificial intelligence (ai) systems [35]. Periodical presentations of the results and conclusions of internal audits to the heads of the hospital and the Committee of nosocomial infections Control especially leave space for ChatGPt-like solutions [34].…”

“…the ai-based solutions often adapt the shape of agentbased systems (abS) designed to interact with users and autonomously solve well-defined problems of different levels of complexity, which can be linked to communicate and cooperate in the form of multi-agent systems (MaS) in order to be able to address more diverse challenges [33]. Dynamically developing conversational large language models (llM), similar to the wellknown ChatGPt, bring the potential benefits to everyday health promotion, medical education, research and practice, including: faster scientific and science popularising writing that can be individually addressing at a time people belonging to wider and more varied audiences; individual and problem-oriented approach to medicine learning; improvements in healthcare research with rapid theoretical and experimental data collection, aggregation, analysis and presentation, leaving scientific staff more time for creative activities; better organised, less bureaucratic and cheaper in operation patient-oriented health care demanding a low entry-level of health literacy and computer literacy and, at the same time, promoting their elevation in ways easily adapted by patients and medical staff [34].…”

Palliative Care and Multi-Agent Systems: A Necessary Paradigm Shift

“…In an example of one healthcare setting, GUARDIAN, a MAS works on a hierarchal basis in which each agent is split into perception/action, reasoning, and control agents [ 19 ]. There have also been proposals to have a hybrid MAS/E-health system to allow for greater efficiency in remote physician appointments, leading to more wholistic care [ 20 ]. One heavily researched MAS model for healthcare is the blackboard model seen below in Figure 2 [ 21 ].…”

Digital Transformation in Health Care and Its Marketing Dimension

“…Telehealth technology is also essential in developing countries (Bohr & Memarzadeh, 2020). In addition, it passes the test in monitoring and observing elderly and disabled patients who live far from healthcare centres (Finco et al, 2023).…”