Artificial Intelligence Systems Research Articles

Improvement of Self-Driven Nanowire-Based Ultraviolet Photodetectors by Metal-Organic Frameworks for Controlling Humanoid Robots.

Self-driven photodetectors (PDs) hold significant potential for the development of new information devices, which boast the advantages of ultralow power consumption and straightforward fabrication. In this study, we have proposed and demonstrated a self-driven ultraviolet PD utilizing gallium nitride/metal-organic framework (GaN/MOF) heterojunction nanowires successfully. By introducing Gd-ETTC MOFs on the surface of GaN nanowires, the photocurrent and responsivity of the device can be improved by approximately 75% under 310 nm illumination. Furthermore, they can also be effectively enhanced under visible light illumination. Owing to the appropriate energy level alignment, Gd-ETTC MOFs can serve as both a light harvester and a hole conductor, facilitating the efficient absorption, separation, and transmission of photogenerated carriers. It has been observed that due to reduced interface resistance, MOFs can enhance the charge transport through the acceleration of charge transfer. Furthermore, the PD equipped with MOFs is capable of continuous operation for 30,000 s, a feat attributable to the exceptional stability of both GaN nanowires and Gd-ETTC MOFs. By implementation of the humanoid robot systems, the control commands from the self-driven PD can drive the humanoid robot to execute different actions. The PD-equipped autonomous feedback system of a humanoid robot enables a seamless integration of light perception with intelligent robotic actions. Therefore, the design and demonstration of GaN/MOF nanowires hold significant reference value for further enhancing the performance of PDs and broadening their applications in ultralow-power artificial intelligence systems, humanoid intelligent robots, etc.

Minimizing Carbon Emissions by Improving Water and Energy Use Efficiencies in AI Servers: A Green Cloud Computing Strategy for Sustainable Artificial Intelligence Systems

The advent of Artificial Intelligence systems, in particular of generative models like ChatGPT, has resulted in one more area requiring heavy computational resources which in turn consumes a lot of energy and water. By estimations, one interaction with ChatGPT for instance will take an estimate of 2.9 watt hour, which is ten times higher than the amount of energy consumed to conduct an ordinary googling task that is 0.3 watt hours. This stands as a call for action toward improving the water to energy ratio of the AI systems and therefore the recent carbon emissions. This paper explores the energy efficiency patterns of AI languages such as chatbots compared with the other means of searching the internet like Google and how the effects of the AI machines on the environment can be reduced. In this connection, green cloud computing methods have been suggested as possible solutions that can be effectively combined with the principles of clean energy use; on this list are both advanced systems for maintaining low temperatures and the optimization of AI systems. Finally, using of resources could also play a crucial part in the ultimate decrease in the adverse effects that the AI industry has on our environment.

Twenty-four years of empirical research on trust in AI: a bibliometric review of trends, overlooked issues, and future directions

AbstractTrust is widely regarded as a critical component to building artificial intelligence (AI) systems that people will use and safely rely upon. As research in this area continues to evolve, it becomes imperative that the research community synchronizes its empirical efforts and aligns on the path toward effective knowledge creation. To lay the groundwork toward achieving this objective, we performed a comprehensive bibliometric analysis, supplemented with a qualitative content analysis of over two decades of empirical research measuring trust in AI, comprising 1’156 core articles and 36’306 cited articles across multiple disciplines. Our analysis reveals several “elephants in the room” pertaining to missing perspectives in global discussions on trust in AI, a lack of contextualized theoretical models and a reliance on exploratory methodologies. We highlight strategies for the empirical research community that are aimed at fostering an in-depth understanding of trust in AI.

Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study.

Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user's login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown. This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series. We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02. In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002). The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence-enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.

A-173 Evaluation of Artificial Intelligence (AI)-assisted Flow Cytometry Analysis for Plasma Cell Neoplasms

Abstract Background Flow cytometry plays a critical role in diagnosing hematologic disorders, yet manual analysis is time-consuming and prone to variability. Significant advancements in artificial intelligence (AI) within healthcare have occurred in recent years. Previously, we developed and evaluated an AI-assisted automated system for diagnosing acute leukemia. This study aims to assess the effectiveness of the AI system in diagnosing plasma cell neoplasms. Methods A total of 76 bone marrow samples were analyzed using a panel of antibodies (CD38, CD56, CD138, Kappa, and Lambda) to determine plasma cell percentages and immunophenotype. All samples were used for initial diagnosis of plasma cell neoplasm or therapeutic monitoring. Manual analysis using Kaluza™ software served as the gold standard. The same flow cytometry data was processed by the AI software (DeepFlow™) for comparison. Pearson correlation coefficients were used to compare AI and manual analysis for diagnosis and plasma cell count. Results Among the 76 cases, manual analysis identified 57 positive cases for monoclonal plasma cells (36 cases of CD38+/CD138+/Kappa+ and 21 cases of CD38+/CD138+/Lambda+), with 89% showing aberrant CD56 expression. AI identified 49 monoclonal cases, missing 8 cases with low plasma cell levels (&amp;lt;0.07%) or brighter fluorescence. AI accurately matched 97% of immunophenotypes with manual analysis. Pearson correlation coefficients demonstrated excellent correlation (r=1.0) between manual and AI plasma cell percentages. Furthermore, AI has also enhanced laboratory efficiency, reducing analysis time per case from 10 minutes with manual methods to just 3 minutes with AI analysis. Conclusions This study shows promise for AI-assisted flow software in diagnosing plasma cell neoplasms. AI has enhanced operational efficiency by reducing the manual processing and analysis time. Further training is needed to improve the identification of variations in fluorescence intensity and low plasma cell levels.

Prototype of a Virtual Assistant System Integrated with AI

This research focuses on the development of a prototype virtual assistant system integrated with artificial intelligence (AI) using the Rational Unified Process (RUP) method. The system is designed to improve user flexibility and efficiency by allowing interaction through voice commands, removing the need for traditional input devices. Python was selected as the primary programming language due to its robust capabilities in handling AI-driven applications. The system utilizes Whisper API for speech recognition, enabling the virtual assistant to accurately interpret voice inputs. Additionally, the integration of Chat GPT API allows the assistant to process and generate responses in a natural, context-aware manner. The combination of these technologies is expected to enhance user experience by making the system more intuitive and seamless, applicable to both daily tasks and complex business environments. The RUP method, structured into phases such as inception, elaboration, construction, and transition, was applied to ensure that the development process was iterative, flexible, and aligned with user needs. The results indicate that the integration of Whisper API with Chat GPT API significantly improves the quality and accuracy of voice-based interaction, streamlining system operation while minimizing the need for complex graphical interfaces. This research demonstrates the potential of voice-driven AI systems in increasing overall operational efficiency.

From roadmap to regulation: will there be a transatlantic approach to governing artificial intelligence?

ABSTRACT The transformative nature and accelerating pace of development of Artificial Intelligence (AI) technologies have generated frenzied political debate and urgent calls for regulation. The European Union’s (EU) AI Act is one of the earliest, most ambitious, legally binding efforts to regulate high-risk AI systems. This article argues that the thrust of the EU’s focus on ‘ethical and secure’ AI and its first-mover regulatory status have helped to foster transatlantic cooperation on AI governance. To probe this assertion, the article analyzes the ethical underpinnings and key provisions of the AI Act and compares them to policy developments in the United States. The analysis provides preliminary evidence of policy similarities across the Atlantic from an alignment of ethical concerns to a shared understanding of the risks and associated need for common guidelines and standards in the AI sector. Tracing agreements within the EU-US Trade and Technology Council (TTC) suggests further cooperation and relative congruence in the EU and US approaches to AI.

Trustworthy AI: AI developers’ lens to implementation challenges and opportunities

As organizations continue to embrace the use of artificial intelligence (AI) systems, it is crucial to ensure that these AI systems can be trusted. However, there is still a significant gap between research on trustworthy AI and its implementation in real-world applications. To address this issue, we sought to explore the perspectives of AI developers and the challenges they face in creating trustworthy AI systems. This exploratory study involved conducting interviews with 19 AI developers. We identified key challenges faced by AI developers due to the immature state of trustworthy AI, inconsistent global regulatory landscape, a lack of standardized definitions of key concepts, limited tools and standards for practical implementation in organizations. This paper provides recommendations for organizations to invest in trustworthy AI processes and practices, this includes building a foundation for trustworthy AI specific to their organization, adopting an organizational approach to trustworthy AI culture, and providing proper data infrastructures to support AI developers in creating trustworthy AI systems. By investing in trustworthy AI practices, organizations can prepare for evolving regulations and ensure that their AI systems are reliable and trustworthy.

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.

AI and Trust in Organizations

As the use of artificial intelligence (AI) expands in both society and organizations, building and maintaining trust in AI systems is becoming increasingly important. While AI promises benefits like improved efficiency and data-driven decision making, concerns are growing around a lack of transparency, accountability, fairness and control over how data is collected and used. This erosion of trust poses a threat to the widespread adoption of AI. However, the article argues that organizations have an opportunity to proactively build trust through transparent, accountable and fair development and application of AI. Drawing from research findings, it outlines strategies for providing transparency into AI systems and decision making, ensuring accountability in development and deployment, and engaging stakeholders. If implemented, these practices can help alleviate fears, demonstrate a commitment to responsible and ethical use of AI, and gain greater public acceptance and adoption of the technology's potential benefits.

Artificial Intelligence Plays Crucial Role in Education Sector of Assam

“AI is a mirror, reflection not only our intellect, but our values and fears” Artificial intelligence (AI) is shaping the future of technology and revolutionizing our education system. Due to evolution of artificial intelligence, we have seen the drastic change in our educational sector throughout the globe. It indicates a positive signal for present and future generations of our country and also throughout the globe. Artificial Intelligence (AI) can play a crucial role in the school functioning process, streamlining and optimizing the procedure for both institutions and applicants. Through AI-driven algorithms, schools can efficiently sift through a large pool of applicants, identifying the most suitable candidates based on predefined criteria such as student’s enrollment according to their academic performance, extracurricular activities, and personal attributes. One of the most remarkable advantages of employing AI in the admission process is its ability to eliminate human bias, ensuring fairness and equal opportunities for all applicants. AI systems are programmed to assess candidates solely based on their qualifications and merit, reducing the influence of subjective factor, viz., race, gender, or socioeconomic background. We have seen the drastic changes in case of new admission in our School and College for last 3 to 4 years in the state of Assam and country as well. Moreover, AI facilitates personalized and efficient communication with applicants, providing them with updates on their application status and guiding them through the admission process. Virtual assistants powered by AI can promptly address queries from applicants, offering them the support they need. Furthermore, AI enables predictive analysis, allowing schools to forecast future trends in admission and tailor their strategies accordingly. By analyzing past admission data and trends, AI systems can provide valuable insights into the preferences and behaviors of prospective students, helping schools make informed decisions and develop targeted recruitment strategies. AI is revolutionizing the school admission process by making it more efficient, transparent, and fair. By leveraging AI technologies, schools can streamline their admission procedures, improve the candidate experience, and ultimately ensure that the most qualified and deserving students are admitted. In Assam, the education department of Government of Assam has introduced Shiksha Setu App to enhance communication between students, parents, teachers, and educational institutions. It facilitates access to various educational resources, attendance tracking of both teachers and students, exam schedules, leave for teachers, and performance reports. Its significance lies in improving transparency, efficiency, and accessibility in the whole education system of Assam, and as result contributing to better student outcomes and parental involvement.

Introduction to the Special Issues

Abstract Artificial intelligence (AI) is rapidly reshaping our world. As AI systems become increasingly autonomous and integrated into various sectors, fundamental ethical issues such as accountability, transparency, bias, and privacy are exacerbated or morph into new forms. This introduction provides an overview of the current ethical landscape of AI. It explores the pressing need to address biases in AI systems, protect individual privacy, ensure transparency and accountability, and manage the broader societal impacts of AI on labor markets, education, and social interactions. It also highlights the global nature of AI's challenges, such as its environmental impact and security risks, stressing the importance of international collaboration and culturally sensitive ethical guidelines. It then outlines three unprecedented challenges AI poses to copyright and intellectual property rights; individual autonomy through AI's “hypersuasion”; and our understanding of authenticity, originality, and creativity through the transformative impact of AI-generated content. The conclusion emphasizes the importance of ongoing critical vigilance, imaginative conceptual design, and collaborative efforts between diverse stakeholders to deal with the ethical complexities of AI and shape a sustainable and socially preferable future. It underscores the crucial role of philosophy in identifying and analyzing the most significant problems and designing convincing and feasible solutions, calling for a new, engaged, and constructive approach to philosophical inquiry in the digital age.

Ethical Artificial Intelligence Framework Theory (EAIFT): A New Paradigm for Embedding Ethical Reasoning in AI Systems

The rapid development of artificial intelligence (AI) has created several ethical issues, including bias, a lack of transparency, and privacy concerns, demanding the incorporation of ethical governance directly into AI systems. This study introduces the Ethical Artificial Intelligence Framework Theory (EAIFT), a novel approach to incorporating ethical reasoning into AI. It emphasizes real-time oversight, open decision-making, bias detection, and the ability to change ethical and legal norms. EAIFT advocates for establishing "ethical AI watchdogs" that automatically monitor and ensure the ethical operation of AI systems, together with dynamic compliance algorithms that can adapt to regulatory changes. The paradigm also encourages transparency and explainability to build user trust and detect and correct biases to ensure fairness. This paper employs a qualitative methodology that combines stakeholder interviews, content analysis, and expert commentary to evaluate EAIFT's potential to increase ethical accountability in various areas, including healthcare, banking, and criminal justice. The findings suggest that EAIFT outperforms existing ethical frameworks by proactively reducing biases, increasing transparency, and ensuring adherence to ethical standards. While presenting a comprehensive and adaptable technique, the study also acknowledges limitations in empirical testing and the need for additional research to widen EAIFT's applicability to future ethical challenges in artificial intelligence. The paper suggests future research subjects, such as empirical testing in different scenarios, a more in-depth examination of ethical risks, and the inclusion of the framework into new AI technologies to promote responsible AI governance by societal norms and values.

Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction

ObjectiveTo assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously. Patients and MethodsIn this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 2022 to April 2024. ResultsThis streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably. ConclusionBy implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.

AI-Based Risk Assessments in Forensic Auditing: Benefits, Challenges and Future Implications

Forensic auditing is a critical component of ensuring financial integrity and detecting fraud within organizations. Traditional methods of risk assessment in forensic auditing often rely on manual processes, which can be time-consuming, labour-intensive, and prone to human error. In recent years, the integration of artificial intelligence (AI) techniques has revolutionized the field, offering more efficient and accurate risk assessment capabilities. This abstract explores the role of AI-based risk assessment in forensic auditing, highlighting its benefits, challenges, and future implications. AI-based risk assessment leverages advanced algorithms and machine learning models to analyse large volumes of financial data, identify patterns, anomalies, and potential red flags indicative of fraudulent activities. By automating repetitive tasks such as data collection, classification, and analysis, AI streamlines the auditing process, enabling forensic auditors to focus on interpreting results and making informed decisions. One of the primary advantages of AI-based risk assessment is its ability to detect complex fraud schemes that may go unnoticed by traditional methods. Machine learning algorithms can detect subtle deviations from expected behaviour, flagging transactions or activities that exhibit unusual patterns or characteristics. Moreover, AI systems can adapt and learn from new data, continuously improving their detection capabilities over time. Another benefit of AI-based risk assessment is its scalability and efficiency. With the increasing volume and complexity of financial transactions, manual auditing processes struggle to keep pace. AI, on the other hand, can analyse vast datasets in a fraction of the time it would take a human auditor, allowing organizations to conduct more comprehensive and timely audits.

Extended Certainty Factors utilizing Neutrosophic Logic

Addressing uncertainty is a significant challenge in the field of artificial intelligence (AI). AI systems often encounter uncertainty due to incomplete data, ambiguous information, or inherent unpredictability in specific situations. To tackle this uncertainty, various solutions have been developed. These solutions range from probabilistic approaches like Bayesian networks and Monte Carlo methods to fuzzy logic and neural networks. These strategies enable AI systems to model and make judgments amid ambiguity by assigning probabilities, dealing with imprecise data, or utilizing learning processes that adapt to changing and uncertain contexts. However, probabilistic methods have limitations when it comes to handling uncertainty. These limitations include assumptions of independence, computational complexity, difficulty in capturing subjectivity, and interpretability. To address these limitations, other methods have been proposed, such as the model of certainty factors. This model offers a framework for reasoning under uncertainty by assigning a numerical value to statements or propositions. Additionally, neutrosophic logic extends classical logic by incorporating the concept of indeterminacy through truth/indeterminacy/falsity-membership functions. In this paper, we propose a method for investigating how certainty factors adapt to a neutrosophic environment. This method contributes to the development of more robust and adaptable decision support systems capable of dealing with diverse uncertainty. Our method, introduced for the first time in related literature, addresses issues such as the limited handling of indeterminacy, the inability to address contradictions, and the limited binary representation of uncertainty that characterize the model of certainty factors. We will provide a clear illustration of these implications through an example that demonstrates the superiority of our suggested method over the traditional technique of certainty factors.

Avoidable biopsies? Validating artificial intelligence–based decision support software in indeterminate thyroid nodules

BackgroundMultiple artificial intelligence (AI) systems have been approved to risk-stratify thyroid nodules through sonographic characterization. We sought to validate the ability of one such AI system, Koios DS (Koios Medical, Chicago, IL), to aid in improving risk stratification of indeterminate thyroid nodules. MethodsA retrospective single-institution dataset was compiled of 28 cytologically indeterminate thyroid nodules having undergone molecular testing and surgical resection, with surgical pathology categorized as malignant or benign. Nodules were retrospectively evaluated with Koios DS. After nodule selection, automated and AI-adapter–derived Thyroid Imaging Reporting and Data System (TI-RADS) levels were recorded, and agreement with radiologist-derived levels was assessed using Cohen’s κ statistic. The performance of malignancy classification was compared between the radiologist and AI-adapter. Biopsy thresholds were re-evaluated using the AI-adapter. ResultsIn this cohort, 7 (25%) nodules were malignant on surgical pathology. The median nodule size was 2.4 cm (interquartile range: 1.8–2.9 cm). Median radiologist and automated TI-RADS levels were both 4, with κ 0.25 (“fair agreement”). Malignancy classification by the radiologist provided sensitivity 100%, specificity 33.3%, positive predictive value (PPV) 33.3%, and negative predictive value (NPV) 100%, compared with the AI-adapter’s performance with sensitivity 85.7%, specificity 76.2%, PPV 54.5%, and NPV 94.1%. Using the AI-adapter, 14 of 28 biopsies would have been deferred, 13 of which were surgically benign. ConclusionKoios automated and radiologist-derived TI-RADS levels were in consistent agreement for indeterminate thyroid nodules. Malignancy reclassification with the AI-adapter improved PPV at minimal cost to NPV. Risk stratification with the addition of the AI-adapter may allow for more accurate patient counseling and the avoidance of biopsies in select cases that would otherwise be cytologically indeterminate.

Technology And Innovation In HR

In India, the high cost of medical education, particularly for an MBBS degree, poses significant challenges for new doctors, who often find that their qualifications do not justify the Expenses incurred. This financial strain is exacerbated when these highly trained professionals are employed for routine tasks such as insurance treatment and billing verification, roles that are not commercially viable given their level of education and potential. As a cost-effective measure, many insurance companies and third-party administrators (TPAs) opt to employ doctors with BHMS or BMS degrees for these tasks. However, these professionals are often underqualified for the complexities of medical verification, leading to inefficiencies and errors in treatment validation and anomaly detection. The situation also adversely affects programs like the Ex-Servicemen Contributory Health Scheme (ECHS), where the need for accurate and efficient cashless medical services is critical. Delays and inaccuracies in medical verification can complicate the provision of timely healthcare to veterans and their families, underscoring the need for improvement in these processes. This paper proposes the implementation of an Artificial Intelligence (AI) ecosystem designed to function as highly qualified medical agents. This AI system will be capable of independently verifying diagnoses, treatment plans, and medical reports for accuracy while also identifying procedural anomalies. The primary objective is to augment the capabilities of the current workforce, enhancing overall efficiency and effectiveness in medical management. By deploying AI medical agents, the project aims to not only address the gap in qualification and task complexity but also improve the accuracy and reliability of medical administrative processes. This initiative promises to revolutionize the way medical verifications are handled, providing a sustainable, scalable solution to a pressing healthcare management issue in India.

Artificial Intelligence And Its Implications In Health Care: A Textual Analysis

This all-inclusive research paper plunges into the transformative nature of artificial intelligence (AI) in healthcare, emphasizing its ability to change patient care and administrative activities. The investigation examines systematically how AI technologies are creating major improvements towards patients’ outcomes and enhanced operational efficiency within the healthcare systems. From diagnostics to personalized treatment plans and administrative tasks, AI is being integrated using sophisticated algorithms in numerous aspects of healthcare delivery. One major focus is improving diagnostic accuracy. AI-backed diagnostic tools such as image recognition software as well as predictive models have outperformed traditional methods in medical condition identification and diagnosis. For instance, artificial intelligence algorithms show exceptional skills in analysing medical images resulting to earlier detection as well as accurate diagnoses of diseases like cancer, cardiovascular conditions among others neurological disorders. These enhancements not only allow for timely intervention but they significantly reduce instances where diagnosis goes wrong hence enhancing patient prognosis while reducing health costs drastically. The ability to customize personalized therapy is yet another essential aspect of AI in healthcare. With such information as genetic data, medical records and lifestyle factors, and other patient information, AI systems can develop individualized treatment plans. It is for this reason that personalization of treatments enhances their efficiency while reducing the risk of any adverse effects as well as ensuring patients’ adherence to prescribed therapies. Besides, it is important to note that AI-driven predictive models are helpful in identifying individuals with a higher probability of developing specific conditions thereby allowing interventions aimed at preventing diseases and bettering overall population health. AI has also transformed administrative tasks within hospitals besides its clinical applications. For example, AI driven automation is now used to streamline scheduling of appointments or billing processes or even storage of patient records which are normally considered routine administrative works. In addition, through these means there is reduced burden on administrative duties for healthcare workers who continue to perform them with increased precision and timeliness. The net effect is that time and resources can be redirected towards more direct patient care thus improving service quality across the board’s entire system

AI Privacy in Context: A Comparative Study of Public and Institutional Discourse on Conversational AI Privacy in the US and Chinese Social Media

The proliferation of conversational artificial intelligence (AI) systems, such as chatbots, has sparked widespread privacy concerns. Previous research suggests that privacy perceptions and practices vary across sociocultural contexts. This study examines public and institutional discourses on conversational AI privacy in the United States and China. Semantic network and discourse analyses of privacy-related discussions on Twitter and Weibo reveal divergent patterns. On Twitter, public discourse emphasizes privacy risks and concerns and advocates for systemic changes, while institutional discourse promotes individualistic approaches to privacy protection. Conversely, on Weibo, public discourse is less focused on privacy risks and more on the positive impacts of AI, aligning closely with institutional narratives. These variations are intertwined with the cultural, political, economic, and regulatory contexts of the two countries. Our study underscores the importance of multi-level analysis in comparative privacy research to provide a holistic view of privacy in various contexts.