Artificial Intelligence Systems Research Articles

The role of digital technologies and Human-Computer Interaction for the future of education

Abstract Over the last 50 years education has been transformed by digital technologies. Many efforts have been made to create and apply “digital” teaching and learning methods, tools and platforms. The last 25 years of computer-based education can be characterized by the availability of digital information sources and the implementation and operation of digital learning management platforms based on the Internet. The question about further meaningful and effective progress in the next decades is openly discussed. Many expect strong influences and changes from artificial intelligence systems that generate contextualized information from various sources. Some see interactive virtual worlds expanding and partially replacing the physical world. Many believe in the further development of learning management and communication platforms. Others do not expect much valuable change at all due to the slow pace of complex educational systems with recent studies even showing a decline in the quality of educational outcomes during the last five years as a result of even too much digitalization in education. This paper discusses these positions with an emphasis on the roles of humans and computers and their interfaces, i.e. Human-Computer Interaction, for future learning and teaching with rapidly changing information technologies in the next 25 and 50 years.

Development and validation of a novel colonoscopy withdrawal time indicator based on YOLOv5.

The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT). Colonoscopy video data from 472 patients across two hospitals were retrospectively analyzed. WTS was computed through a combination of artificial intelligence (AI) and manual verification. The results obtained through WTS were compared with those generated by the AI system. Patients were categorized into four groups based on the presence of polyps and whether resections or biopsies were performed. Bland Altman plots were utilized to compare AI-computed WTS with manually verified WTS. Scatterplots were used to illustrate WTS within the four groups, among different hospitals, and across various physicians. A parallel box plot was employed to depict the proportions of WTS relative to WT within each of the four groups. The study included 472 patients, with a median age of 55years, and 57.8% were male. A significant correlation with manually verified WTS (r=0.918) was observed in AI-computed WTS. Significant differences in WTS/WT among the four groups were revealed by the parallel box plot (P<0.001). The group with no detected polyps had the highest WTS/WT, with a median of 0.69 (interquartile range: 0.40, 0.97). WTS patterns were found to be varied between the two hospitals and among senior and junior physicians. A promising alternative to traditional WT for quality control and training assessment in colonoscopy is offered by AI-assisted computation of WTS.

Contextual knowledge graph approach to bias-reduced decision support systems

ABSTRACT Recent research shows that automated decision-making systems based on Artificial Intelligence (AI) may lead to perceived unfairness and bias, especially in some sensitive areas. There is no universal debiasing solution for AI applications and systems. This paper proposes a bias-reducing framework based on contextual knowledge graphs for decision-making systems to help analyse and detect potential bias factors during system operation in near-real time. In particular, the contextual knowledge graph is designed to learn the relations between current tasks and corresponding features and explore the correlation among data, context and tasks. Three bias assessment metrics (i.e., label bias, sampling bias and timeliness bias) are proposed to measure, quantify and qualitatively define the bias level for the pre- and post-modelling. The trained model using debiased datasets combines contextual knowledge to support fairer decision-making. Experimental results show that the proposed method is more effective in supporting fairer decision- making than the existing methods.

College Chatbot

Nowadays, many people are using smartphones with many new applications i.e., technology is growing day by day. Today Artificial Intelligence is playing a major role in a variety of fields ranging from industries in product manufacturing, to customer care in public relations. There are many online Artificial Intelligence (AI) systems or chatbots which are in existence that help people solve their problems. So, we are going to implement a virtual assistant based on AI that can solve any college-related query. This will work as a College Oriented Intelligence machine. This virtual machine will respond to the queries of students on college-related issues. A chatbot has information stored in its database to identify the sentences and make a decision itself as a response to answer a given question. The college enquiry chatbot will be built using an algorithm that analyses queries and understands user’s messages. College Enquiry Chatbot is a type of web application, the main motto of this project is to provide information regarding college. It gives information regarding college only. The information may be like how many branches are in college? what are the courses offered by the college? how many students per branch? By adding some more features and making some improvements we can embed this project into the working site of the college. The chatbot created here is a web-based application which uses Natural Language Processing Libraries and Artificial Intelligence Language to have conversations with humans. It is a simple bot which answers the queries regarding the college

GI genius endoscopy module: a clinical profile

ABSTRACT Introduction The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. Areas covered This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. Expert opinion Randomized controlled trials (RCTs) indicate that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, the contribution of GI Genius appears to be mainly related to detection of non-advanced, diminutive polyps, while it doesn’t seem to affect the identification of advanced and difficult-to-detect adenomas. When using GI Genius, there were no documented side-events, with only a small number of studies reporting an increased withdrawal time or an increased removal of non-neoplastic lesions.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception.

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception. This article is protected by copyright. All rights reserved.

The scientific knowledge of three large language models in cardiology: multiple choice questions examination-based performance

Background: The integration of artificial intelligence (AI) chatbots like Google’s Bard, OpenAI’s ChatGPT, and Microsoft’s Bing Chatbot into academic and professional domains, including cardiology, has been rapidly evolving. Their application in educational and research frameworks, however, raises questions about their efficacy, particularly in specialized fields like cardiology. This study aims to evaluate the knowledge depth and accuracy of these AI chatbots in cardiology using a multiple-choice question (MCQ) format. Methods: The study was conducted as an exploratory, cross-sectional study in November 2023 on a bank of 100 MCQs covering various cardiology topics that was created from authoritative textbooks and question banks. These MCQs were then used to assess the knowledge level of Google’s Bard, Microsoft Bing, and ChatGPT 4.0. Each question was entered manually into the chatbots, ensuring no memory retention bias. Results: The study found that ChatGPT 4.0 demonstrated the highest knowledge score in cardiology, with 87% accuracy, followed by Bing at 60% and Bard at 46%. The performance varied across different cardiology subtopics, with ChatGPT consistently outperforming the others. Notably, the study revealed significant differences in the proficiency of these chatbots in specific cardiology domains. Conclusion: This study highlights a spectrum of efficacy among AI chatbots in disseminating cardiology knowledge. ChatGPT 4.0 emerged as a potential auxiliary educational resource in cardiology, surpassing traditional learning methods in some aspects. However, the variability in performance among these AI systems underscores the need for cautious evaluation and continuous improvement, especially for chatbots like Bard, to ensure reliability and accuracy in medical knowledge dissemination.

A semi-automated software model to support AI ethics compliance assessment of an AI system guided by ethical principles of AI

AbstractCompliance with principles and guidelines for ethical AI has a significant impact on companies engaged in the development of artificial intelligence (AI) systems. Specifically, ethics is a broad concept that continuously evolves over time and across cultural and geographical boundaries. International organisations (IOs), individual states, and private groups, all have an interest in defining the concept of ethics of AI. IOs, as well as regional and national bodies, have issued many decisions on AI ethics. Developing a system that complies with the ethical framework poses a complex challenge for companies, and the consequences of not complying with ethical principles can have severe consequences, making compliance with these requirements a key issue for companies. Furthermore, there is a shortage of technical tools to ensure that such AI systems comply with ethical criteria. The scarcity of ethics compliance checking tools for AI, and the current focus on defining ethical guidelines for AI development, has led us to undertake a proposal consisting in a semi-automated software model to verify the ethical compliance of an AI system’s code. To implement this model, we focus on the following important aspects: (1) a literature review to identify existing ethical compliance systems, (2) a review of principles and guidelines for ethical AI to determine the international and European views regarding AI ethics, and (3) the identification of commonly accepted principles and sub-principles of AI. These elements served to inform (4) our proposal for the design of a semi-automated software for verifying the ethical compliance of AI systems both at design-time (ethics-by-design perspective) and afterwards on the resulting software.

The fine line between automation and augmentation in website usability evaluation

Artificial Intelligence (AI) systems are becoming widespread in all aspects of society, bringing benefits to the whole economy. There is a growing understanding of the potential benefits and risks of this type of technology. While the benefits are more efficient decision processes and industrial productivity, the risks may include a potential progressive disengagement of human beings in crucial aspects of decision-making. In this respect, a new perspective is emerging that aims at reconsidering the centrality of human beings while reaping the benefits of AI systems to augment rather than replace professional skills: Human-Centred AI (HCAI) is a novel framework that posits that high levels of human control do not contradict high levels of computer automation. In this paper, we investigate the two antipodes, automation vs augmentation, in the context of website usability evaluation. Specifically, we have analyzed whether the level of automation provided by a tool for semi-automatic usability evaluation can support evaluators in identifying usability problems. Three different visualizations, each one corresponding to a different level of automation, ranging from a full-automation approach to an augmentation approach, were compared in an experimental study. We found that a fully automated approach could help evaluators detect a significant number of medium and high-severity usability problems, which are the most critical in a software system; however, it also emerged that it was possible to detect more low-severity usability problems using one of the augmented approaches proposed in this paper.

Foundation models meet visualizations: Challenges and opportunities

AbstractRecent studies have indicated that foundation models, such as BERT and GPT, excel at adapting to various downstream tasks. This adaptability has made them a dominant force in building artificial intelligence (AI) systems. Moreover, a new research paradigm has emerged as visualization techniques are incorporated into these models. This study divides these intersections into two research areas: visualization for foundation model (VIS4FM) and foundation model for visualization (FM4VIS). In terms of VIS4FM, we explore the primary role of visualizations in understanding, refining, and evaluating these intricate foundation models. VIS4FM addresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, in terms of FM4VIS, we highlight how foundation models can be used to advance the visualization field itself. The intersection of foundation models with visualizations is promising but also introduces a set of challenges. By highlighting these challenges and promising opportunities, this study aims to provide a starting point for the continued exploration of this research avenue.

Automated precise seeding with drones and artificial intelligence: a workflow

Aerial seeding with drones has great potential in forest restoration but faces enormous challenges to be efficient and scalable. Current protocols use blanket seeding throughout the area to be restored, meaning a high demand for seed since many seeds arrive in sites unsuitable for establishment. High precision seeding directed to safe microsites at submeter scale could reduce seed use per hectare, reducing economic and ecological costs, while increasing establishment success. Here, we propose an alternative, precision approach to make drone seeding more successful and efficient. This requires (1) submeter‐scale selection of target microsites for seeding founded in ecological knowledge; (2) high‐resolution remote sensing imagery to train artificial intelligence (AI) systems in target microsite recognition; and (3) process automation by transferring target microsite coordinates from the AI system to the drone. This will reduce seed inputs per unit area, seedling establishment failure risks, and drone operation costs.

Determinants of Public Sector Managers' Intentions to Adopt AI in the Workplace

This study investigated the determinants of public sector managers' intentions to adopt artificial intelligence (AI) systems within their organizations. An extended technology acceptance model (TAM) was developed, incorporating additional constructs including fairness, humanity, reliability, safety, transparency, accountability, privacy, security, trust, social norms, tolerance, impact, and isomorphic pressure. A survey was conducted among 330 public sector managers, and the data were analyzed using linear regression tests to evaluate the model. The results showed significant positive influences of both perceived usefulness and perceived impact on managers' attitudes and behavioral intentions toward AI adoption. Isomorphic pressure was also a significant determinant of managers' behavioral intentions toward adopting AI systems. Our findings also indicated that perceptions related to AI ethical principles, such as transparency, privacy, and security, influenced managers' trust in AI systems.

Artificial Intelligence-Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to Clinical Practice.

To explore the potential of artificial intelligence (AI) to assist prescription determination for orthokeratology (OK) lenses. Artificial intelligence algorithm development followed by a real-world trial. A total of 11,502 OK lenses fitting records collected from seven clinical environments covering major brands. Records were randomly divided in a three-way data split. Cross-validation was used to identify the most accurate algorithm, followed by an evaluation using an independent test data set. An online AI-assisted system was implemented and assessed in a real-world trial involving four junior and three senior clinicians. The primary outcome measure was the algorithm's accuracy (ACC). The ACC of the best performance of algorithms to predict the targeted reduction amplitude, lens diameter, and alignment curve of the prescription was 0.80, 0.82, and 0.83, respectively. With the assistance of the AI system, the number of trials required to determine the final prescription significantly decreased for six of the seven participating clinicians (all P<0.01). This reduction was more significant among junior clinicians compared with consultants (0.76±0.60 vs. 0.32±0.60, P<0.001). Junior clinicians achieved clinical outcomes comparable to their seniors, as 93.96% (140/149) and 94.44% (119/126), respectively, of the eyes fitted achieved unaided visual acuity no worse than 0.8 (P=0.864). AI can improve prescription efficiency and reduce discrepancies in clinical outcomes among clinicians with differing levels of experience. Embedment of AI in practice should ultimately help lessen the medical burden and improve service quality for myopia boom emerging worldwide.

AI takeover and human disempowerment

Abstract Some take seriously the possibility of artificial intelligence (AI) takeover, where AI systems seize power in a way that leads to human disempowerment. Assessing the likelihood of takeover requires answering empirical questions about the future of AI technologies and the context in which AI will operate. In many cases, philosophers are poorly placed to answer these questions. However, some prior questions are more amenable to philosophical techniques. What does it mean to speak of AI empowerment and human disempowerment? And what empirical claims must hold for the former to lead to the latter? In this paper, I address these questions, providing foundations for further evaluation of the likelihood of takeover.

Artificial Intelligence in Judicial Adjudication: Semantic Biasness Classification and Identification in Legal Judgement (SBCILJ)

History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

EDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms

ObjectiveDespite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Considering the vast amount of data that composes the EEG, much of the detail inevitably escapes ordinary human scrutiny. Significant information may not be evident and is missed, and misinterpretation remains a serious problem. Can we develop an artificial intelligence system to accurately and efficiently classify EEG and even reveal novel information? In this study, deep learning techniques and, in particular, Convolutional Neural Networks, have been used to develop a model (which we have named eDeeplepsy) for distinguishing different brain states in children with epilepsy. MethodsA novel EEG database from a homogenous pediatric population with epileptic spasms beyond infancy was constituted by epileptologists, representing a particularly intriguing seizure type and challenging EEG. The analysis was performed on such samples from long-term video-EEG recordings, previously coded as images showing how different parts of the epileptic brain are distinctly activated during varying states within and around this seizure type. ResultsResults show that not only could eDeeplepsy differentiate ictal from interictal states but also discriminate brain activity between spasms within a cluster from activity away from clusters, usually undifferentiated by visual inspection. Accuracies between 86 % and 94 % were obtained for the proposed use cases. SignificanceWe present a model for computer-assisted discrimination that can consistently detect subtle differences in the various brain states of children with epileptic spasms, and which can be used in other settings in epilepsy with the purpose of reducing workload and discrepancies or misinterpretations. The research also reveals previously undisclosed information that allows for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. It does so by documenting a different state (interspasms) that indicates a potentially non-standard signal with distinctive epileptogenicity at that period.

Process-in-Memory realized by nonvolatile Task-Scheduling and Resource-Sharing XNOR-Net hardware Accelerator architectures

Due to the success of artificial intelligence systems in solving complex problems, these systems have been investigated in many applications. There is an increasing demand for these systems in many new applications. The XNOR-Net, a binary neural network, is a promising candidate for implementing these systems in devices with limited power and hardware resources. Efficient hardware implementation of XNOR-Nets is critical and has been widely investigated in recent research. In this paper, a resource-sharing gate and architecture and a task-scheduling gate and architecture have been proposed. To reduce power consumption, the proposed nonvolatile logic-in-memory gates are designed based on the magnetic tunnel junction (MTJ) and carbon nanotube field-effect transistor (CNTFET) devices. Using the proposed resource-sharing gate for hardware implementation of XNOR-Net minimizes the number of flip-flops, power consumption, PDP, and area by at least 75%, 75%, 60%, and 42%, respectively, as compared to its state-of-the-art counterparts. Also, using the proposed task-scheduling design to implement the XNOR-Net on 2, 4, and 8 datasets, the power consumption is reduced by at least 24%, 54%, and 65%, respectively, compared to its state-of-the-art counterparts .

Investigating awareness of artificial intelligence in healthcare among medical students and professionals in Pakistan: a cross-sectional study.

The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan's medical community so as to comment on its future in our healthcare system. A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05). A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan's healthcare system. This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals' perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.