TIM-7072 v1

Junaid Baiwa, Usman Munir, Aditya Nori, & Byan Williams. (2022). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal 8(2), e188–e194.
This article summarizes recent breakthroughs in the application of AI in healthcare, describes a roadmap to building effective AI systems and discusses the possible future direction of AI-augmented healthcare systems.

Montejo, L., Fenton, A., & Davis, G. (2024). Artificial intelligence (AI) applications in healthcare and considerations for nursing education. Nurse Education in Practice, 80.
This resource explores a comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

Zahra Amiri, Ali Taghavirashidizadeh, & Parsa Khorrami (2025). AI-driven decision-making in healthcare information systems: A comprehensive review. Journal of Systems and Software.
The research presented a novel taxonomy for AI applications in decision-making in healthcare, classifying them into seven categories: clinical decision support systems, predictive analytics, natural language processing, computer-aided diagnostics, robotic-assisted surgery, virtual health assistants, and drug discovery.

Muhammad Mohsin Khan, Noman Shah, Nissar Shaikh, Abdulnasser Thabet, Talal Alrabayah, & Sirajeddin Belkhair. (2025). Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges. International Journal of Medical Informatics.195.
AI can bring transformative opportunities to improve healthcare outcomes, but successful implementation will depend on overcoming the challenges of trust, security, and ethics. Future research should focus on testing these technologies in multiple real-world settings, enhance their scalability, and fine-tune regulations to facilitate accountability. Only by combining technological innovations with ethical principles and robust governance can AI reshape healthcare, ensuring, at the same time, safety and trustworthiness.

Rajiv Kumar Pathni. (2024). Beyond algorithms: Ethical implications of AI in healthcare. Medical Journal Armed Forces of India.
This paper explores the challenges for AI in healthcare and discusses its ethical implications using the framework of the four core principles of modern bioethics.

Herbosch. M. (2025). To err is human: Managing the risks of contracting AI systems. Computer Law and Security Review. 56.
This article challenges these views by leveraging existing contract formation and mistake frameworks. Traditional analysis demonstrates that AI deployment can produce valid contracts. It also suggests that deployers may invoke the unilateral mistake doctrine, drawing parallels to clerical errors in human contracts.

Tian Wei, Han Wu, Michael Dowling, & Aref Mahdavi Ardekani. (2025). Financial perceptions and AI infringement risks. International Review of Economics and Finance.101.
This study examines the understanding of this emergent risk and its associated implications. To do so, the authors applied social capital theory to an analysis of the reactions of 10,447 Chinese social media users to China's first AI-generated voice infringement lawsuit. The findings suggest that out-tie social capital (exposure to diverse networks) tends to promote neutral or positive views. In contrast, in-tie social capital (strong, close-knit communities) initially encourages favorable attitudes but shifts toward ethical and risk concerns when potential financial damages are perceived. The study thus highlights the interplay between social perception and corporate financial considerations in an era where AI is increasingly shaping economic opportunities and liabilities.

Rami Mohawesh, Mohammad Ashraf Ottom, & Haythem Bany Salameh . (2025). A data-driven risk assessment of cybersecurity challenges posed by generative AI. Decision Analytics Journal, 15.
This study aims to examine how GenAI can enhance cybersecurity by improving AI algorithms and propose strategies for mitigating associated risks. As GenAI continues to gain significance across various industries, particularly in healthcare, the study emphasizes the importance of understanding the potential benefits and risks it may pose to ensure safe and responsible deployment.

Fredrick Ahenkora Boamah, Xiaohua Jin, Sepani Senaratne, & Srinath Perer .(2025). AI-driven risk identification model for infrastructure project: Utilizing past project data. Expert Systems with Applications. 283.
To improve the accuracy of risk identification, this study utilizes historical data in conjunction with AI approaches to develop a data-driven risk identification model. The model determines risk frequency and consequence by matching them to different risk categories in previous projects, taking into account word semantics. This model also facilitates proactive decision-making, enabling infrastructure project team members to identify risks early and implement effective mitigation plans. The study also highlights the practical significance of utilizing historical data to enhance the reliability and efficiency of risk management strategies for infrastructure projects.

Adib Habbal, Mohamed Khalif Ali, Mustafa Ali Abuzaraida (2024). Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions. Expert Systems with Applications. 240.
This paper explores various applications of the AI TRiSM framework across different domains, including finance, healthcare, and the Metaverse. Furthermore, the paper discusses the obstacles related to implementing the AI TRiSM framework, including adversarial attacks, the constantly changing landscape of threats, ensuring regulatory compliance, addressing skill gaps, and acquiring expertise in the field. Finally, it explores the future directions of AI TRiSM, emphasizing the importance of continual adaptation and collaboration among stakeholders to address emerging risks and promote ethical and enhanced overall security bearing for AI systems.

van Leersum, C. M., & Maathuis, C.(2025). Human-centered explainable AI decision-making in healthcare. Journal of Responsible Technology. 21.
A reflective ethical and socio-technical perspective, where technical advancements and human factors co-evolve, is called human-centered explainable AI (HCXAI3). This perspective sets humans at the center of AI design with a holistic understanding of values, interpersonal dynamics, and the socially situated nature of AI systems.

Koscelny, S. N., Sadralashrafi, S., & David M. Neyens, D. M. (2025). Generative AI responses are a dime a dozen; Making them count is the challenge – Evaluating information presentation styles in healthcare chatbots using hierarchical Bayesian regression models. Applied Ergonomics. 128.
The findings revealed that perceptions of low usability significantly decreased the effectiveness of the healthcare chatbot. Additionally, participants exposed to the conversational style of the chatbot had a significantly increased likelihood of perceiving it as more usable. However, they were also less likely to trust the chatbot. These results suggest that varying information presentation styles can impact user experience and provide insights for future research on healthcare chatbots and other AI systems.

Bangul Khan, Rana Talha Khalid, Khair Ul Wara, Muhammad Hasan Masrur, Samiullah Khan, Wasim Ullah Khan, Umay Amara, & Saad Abdullah. (2025). Reshaping the healthcare world by AI-integrated wearable sensors following COVID-19. Chemical Engineering Journal. 505.
This review article focuses on the evolution and integration of AI in wearable sensors following pandemics. These technological breakthroughs have led to the development of contemporary wearable devices that are equipped to monitor various health parameters, anticipate potential health issues, and provide early warnings before an event occurs.

Ruchika Arora, & Ramesh Babu Damarla. (2025). A review on generative AI powered talent management, employee engagement and retention strategies: Applications, benefits, and challenges. Procedia Computer Science. 260, 683-691.
This review examines how GAI is applied to talent management, with a focus on utilizing it in employee engagement and retention strategies. It explores insights into its benefits, challenges, and applications. The paper further extends with a detailed literature review of the various applications of generative AI in talent management, including talent identification, recruitment, training, and performance appraisal. It also considers numerous benefits related to implementing GAI for talent management, such as improved work efficiency, personalized employee experiences, and data-driven decision-making. In this regard, the review highlights some of the complex challenges, including ethical issues, biases in algorithmic decision-making, privacy concerns, and the need to upskill HR professionals that arise from implementing GAI. It does so by showcasing the current opportunities that open up organizations that adopt GAI to optimize their talent management processes, increase employee engagement, and boost retention rates in a rapidly changing digital landscape.

Wilhelm, C., Steckelberg, A., & Felix G. Rebitschek, F. G. (2025). Benefits and harms associated with the use of AI-related algorithmic decision-making systems by healthcare professionals: A systematic review. The Lancet – Regional Health Journal 48.
The authors assessed the risk of bias using Cochrane's RoB 2 and ROBINS-I tools and reporting transparency with CONSORT-AI and TRIPOD-AI. Two researchers independently managed the processes and resolved conflicts through discussion. This review has been registered with PROSPERO (CRD42023412156), and the study protocol has been published.

Grosse, K., & Alahi, A. (2024). A qualitative AI security risk assessment of autonomous vehicles. Journal of Transportation Research: Part C-Emerging Technologies. 169.
This paper systematically analyzes the security risks associated with artificial intelligence (AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for various autonomous vehicle (AV) functions, from perception to control, the potential for security breaches presents a significant challenge. The focus was on AI security, including attacks such as adversarial examples, backdoors, privacy breaches, and unauthorized model replication, as reviewed in over 170 papers.