Survey on cancer patients' attitudes towards AI and data protection: A cross-sectional study from an Italian cancer center.

Alliance Against Cancer (ACC) was established in Rome in 2002 as a consortium of six Italian comprehensive cancer centers (Founders). The aims of ACC were to promote a network among Italian oncologic institutions in order to develop specific, advanced projects in clinical and translational research. During the following years, many additional full and associate members joined ACC, that presently includes the National Institute of Health, 17 research-oriented hospitals, scientific and patient organizations. Furthermore, in the last three years ACC underwent a reorganization process that redesigned the structure, governance and major activities. The present goal of ACC is to achieve high standards of care across Italy, to implement and harmonize principles of modern personalized and precision medicine, by developing cost effective processes and to provide tailored information to cancer patients. We herein summarize some of the major initiatives that ACC is currently developing to reach its goal, including tumor genetic screening programs, establishment of clinical trial programs for cancer patients treated in Italian cancer centers, facilitate their access to innovative drugs under development, improve quality through an European accreditation process (European Organization of Cancer Institutes), and develop international partnerships. In conclusion, ACC is a growing organization, trying to respond to the need of networking in Italy and may contribute significantly to improve the way we face cancer in Europe.

RapidAI Review for Stroke Detection What Is the Issue? Stroke is a sudden loss of neurologic function caused by poor or interrupted blood flow within the brain. It is 1 of the leading causes of death and a major cause of disability in Canada. For patients with suspected stroke, prompt evaluation using CT imaging and other tests can help to determine the type of stroke, to assess the severity of damage, and to guide treatment decisions. RapidAI is an artificial intelligence (AI)–enabled software platform that facilitates the viewing, processing, and analysis of CT images to aid clinicians in assessing patients with suspected stroke. Understanding the potential benefits and harms of using RapidAI is important to clarify its role in stroke detection. What Did We Do? We sought to identify, synthesize, and critically appraise literature evaluating the effectiveness, accuracy, and cost-effectiveness of RapidAI for detecting large-vessel occlusion (LVO) (i.e., ischemic stroke) and intracranial hemorrhage (ICH) (i.e., hemorrhagic stroke). We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published up to July 22, 2024. We screened citations for inclusion based on predefined criteria, critically appraised the included studies, narratively summarized the findings, and assessed the certainty of evidence. Our methods were guided by the Scottish Health Technologies Group’s health technology assessment (HTA) framework. We highlighted and reflected on the ethical and equity implications of using RapidAI for stroke detection, found in the clinical literature, integrating these considerations throughout the review. We engaged a patient contributor who had experienced a hemorrhagic stroke, to learn about her experience, perspectives, and priorities. Additionally, we incorporated feedback from clinical and ethics experts, the manufacturer, and other interested parties. What Did We Find? We found 2 cohort studies and 11 diagnostic accuracy studies that assessed the effectiveness and accuracy of RapidAI for detecting stroke. Among these, 3 studies evaluated RapidAI as it is intended to be used in clinical practice (i.e., to complement clinician interpretation of CT images), while the remaining 10 studies assessed RapidAI as a standalone intervention. The patient contributor identified important outcomes for stroke care, including improving speed and accuracy of diagnosis, minimizing the damaging effects of stroke, and reducing mortality rates. She also highlighted ethical considerations regarding the use of AI in health care, such as providing data privacy and equitable access, as well as informing patients about the use of AI technologies in the care pathway. Low-certainty evidence suggests that evaluation of CT angiography images by Rapid LVO combined with clinician interpretation, compared to clinician interpretation alone, may result in clinically important reductions in radiology-report turnaround time in patients with suspected stroke. For detecting ICH, low-certainty evidence suggests that Rapid ICH combined with clinician interpretation, using clinician interpretation as a reference standard, has a sensitivity of 92% (95% confidence interval [CI], 78% to 98%) and a specificity of 100% (95% CI, 98% to 100%). However, estimates of sensitivity and specificity for detecting LVO varied, based on studies using different modules of RapidAI as a standalone intervention, providing only indirect accuracy data. The effects of RapidAI on other time-to-intervention metrics, measures of physical and cognitive function, and response to therapy (e.g., reperfusion rates) were very uncertain. We did not identify any evidence on the effects of RapidAI on many important clinical outcomes, including patient harms, mortality, health-related quality of life, length of hospital stay, or health care resource implications. We did not find any studies on the cost-effectiveness of RapidAI for detecting stroke that met our selection criteria for this review. Ethical and equity considerations related to patient autonomy, privacy, transparency, access, and algorithmic bias have implications across the technology life cycle when using RapidAI for detecting stroke. What Does This Mean? RapidAI has the potential to improve acute stroke care by creating efficiencies in the diagnostic process. However, the impact of RapidAI on many outcomes, including those that are important to patients, is uncertain due to limitations of the available evidence. To improve the certainty of findings, there is a need for evidence from robustly conducted studies at lower risk of bias that enrol diverse patient populations and measure outcomes that are important to patients, with improved reporting. The cost-effectiveness of RapidAI for stroke detection is currently unknown. In addition to the evidence on the effectiveness and accuracy of RapidAI for detecting stroke, decision-makers may wish to reflect on the ethical and equity considerations that arise during the deployment of AI-enabled technologies, such as those related to autonomy, privacy, transparency, and explainability of machine-learning models, and the need for considerations related to equity and access in their design, development, and deployment. AI Implementation Review What Is the Issue? Globally, we are seeing a widespread increase in the interest, development, and use of artificial intelligence (AI)–enabled medical devices. Comprehensive evaluation through health technology assessment (HTA) can ensure that digital health technologies (DHTs), including AI-enabled medical devices, are adequately equipped to balance benefits and harms, while being interoperable and equitably accessible to people living in Canada. In the UK, a checklist called Digital Technology Assessment Criteria (DTAC) is used as an add-on component to HTAs to capture additional considerations for the implementation of DHTs. The 5 core areas of DTAC are clinical safety, data protection, technical security, interoperability, and usability and accessibility. In Canada, we currently do not have a DTAC equivalent that can be used as an add-on to traditional HTA. This implementation review is needed to assist health systems in Canada in preparing for the uptake of AI-enabled medical devices, as these technologies pose new challenges. We assessed whether the safeguards and assessment criteria captured by DTAC and other AI-related resources are in place to inform decision-making around the digital infrastructure elements of implementation. What Did We Do? We conducted an implementation review, using a phased approach, to determine whether DTAC can be applied to the health care context in Canada to inform the implementation of DHTs and to identify any additional implementation considerations specific to the use of AI-enabled medical devices in Canada. We integrated ethics and equity considerations across both phases of the review. In phase 1, we applied DTAC to the health care context in Canada by determining whether we have equivalent or similar measures, strategies, and policies in place to implement DHTs safely. In phase 2, an information specialist searched for literature to identify implementation guidance specific to AI and relevant to Canada to supplement DTAC. One reviewer screened publications for inclusion based on predefined criteria, incorporated relevant information into tables, and summarized the findings narratively. We leveraged patient engagement activities conducted in a concurrent Canada’s Drug Agency review of a specific AI-enabled medical device in stroke detection to learn from a patient contributor with lived experience of a hemorrhagic stroke. We learned about her experience, perspectives, priorities, and thoughts about using AI in clinical decision-making. What Did We Find? With some caveats, we found that many of DTAC’s assessment criteria have equivalent or similar guidance for the health care context in Canada. Some exceptions are derived from the differences in Canada’s current governance and health care structure. Further investigation is required to understand whether certain policies in Canada provide sufficient coverage to fulfill DTAC’s criteria (e.g., clinical safety). We identified several considerations for implementing AI-enabled medical devices, with many having underlying ethical and equity implications. Much of the identified guidance emphasizes implementation considerations that apply to the AI system’s entire life cycle, including the most prevalent consideration: ensuring AI-enabled medical devices are monitored, maintained, and sustainable. Examples of additional considerations include AI data governance and data protection; transparency and explainability; and inclusiveness, equity, and minimization of bias. The patient contributor highlighted several considerations relevant for this review, such as data protection and privacy as well as accessibility and equity. What Does This Mean? We have identified key considerations for AI-enabled medical devices that health care decision-makers may consider for the safe and successful implementation of AI in health care in Canada. While Canada has DTAC-equivalent or similar measures, strategies, or policies in place, we identified a need for a checklist like DTAC that senior decision-makers can use. This checklist could be an adaptation of DTAC and could include additional implementation considerations for AI-enabled medical devices to ensure that these technologies meet the minimum baseline standards set out by DTAC and inform the next steps for the safe and successful implementation of AI-enabled medical devices in Canada. This implementation review for all AI-enabled medical devices is to be used alongside reviews of specific AI technologies, including the concurrent review of RapidAI, and will serve as a foundational report to be tailored for each AI topic and updated with the latest developments in the regulation and other aspects of management of AI in the context of Canada.

In an era where digital transformation is rapidly reshaping industries, data privacy has emerged as a cornerstone of trust and security. Critical sectors such as healthcare, finance, and government manage vast volumes of sensitive information, necessitating robust mechanisms to safeguard against unauthorized access, cyber threats, and regulatory breaches. The rise of artificial intelligence (AI) presents a paradigm shift in how data privacy is enforced, introducing advanced security solutions while also posing new ethical and technical challenges. This white paper delves into AI’s role in reinforcing data privacy, focusing on key areas such as AI-driven encryption, real-time anomaly detection, predictive analytics, and compliance automation. AI’s ability to analyze vast datasets, detect threats proactively, and enforce policy-driven security measures allows industries to move beyond traditional cybersecurity frameworks toward adaptive and intelligent data protection strategies. We examine how AI enhances cryptographic security, automates risk mitigation processes, and integrates with emerging technologies such as blockchain for decentralized privacy management. Furthermore, AI’s capability to monitor and enforce compliance with regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) provides organizations with a proactive approach to data governance. While AI offers substantial benefits in safeguarding sensitive information, its implementation requires addressing critical concerns such as algorithmic bias, transparency, explainability, and the potential for adversarial attacks. This paper provides a comprehensive review of AI’s impact on data privacy before 2020, drawing from established academic and industry studies. By exploring real-world use cases and ethical implications, we present a balanced perspective on how AI can be leveraged effectively to create a secure digital environment for critical industries.

Artificial Intelligence (AI) is revolutionizing the field of health sciences, reshaping how we teach, learn, and practice medicine. As AI technologies become increasingly integrated into healthcare systems, their impact on health sciences education cannot be overstated. From personalized learning experiences to advanced diagnostic training, AI is poised to enhance the quality and accessibility of education for future healthcare professionals. However, this transformation also raises critical questions about ethics, equity, and the future role of educators in an AI-driven world. The transformative role of Artificial Intelligence (AI) in health sciences education is increasingly recognized as a pivotal factor in shaping the future of medical training and practice. As AI technologies continue to evolve, their integration into educational curricula presents both opportunities and challenges that must be carefully navigated to enhance the learning experience for future healthcare professionals. One of the most significant contributions of AI to health sciences education is its ability to personalize learning. Traditional teaching methods often follow a one-size-fits-all approach, which can leave some students struggling to keep up while others are not sufficiently challenged. AI-powered platforms, such as adaptive learning systems, analyze individual student performance and tailor content to meet their unique needs. For example, tools like Osmosis and AMBOSS use AI to provide customized study plans, ensuring that students focus on areas where they need the most improvement (Topol, 2019). This personalized approach not only improves learning outcomes but also fosters a more inclusive educational environment. AI is also transforming clinical training by simulating real-world scenarios. Virtual patient simulations, powered by AI, allow students to practice diagnosing and treating conditions in a risk-free environment. These simulations can replicate rare or complex cases that students might not encounter during their clinical rotations. For instance, platforms like Touch Surgery and SimX use AI to create immersive surgical and emergency care simulations, providing students with hands-on experience before they enter the operating room (McGaghie et al., 2011). Such tools bridge the gap between theory and practice, preparing students for the complexities of modern healthcare. Moreover, AI is enhancing the role of educators by automating administrative tasks and providing data-driven insights into student performance. Grading, attendance tracking, and even curriculum design can be streamlined using AI, allowing educators to focus on mentoring and engaging with students. AI-driven analytics can also identify at-risk students early, enabling timely interventions to support their academic success (Wartman & Combs, 2018). By augmenting the capabilities of educators, AI empowers them to deliver more impactful and student-centered teaching. AI's potential to revolutionize health sciences education lies in its ability to personalize learning experiences and improve educational outcomes. For instance, AI-driven tools can facilitate realistic simulations and automated assessments, allowing students to engage in practical scenarios that mimic real-world clinical situations (Santos & Lopes, 2024). This capability not only enhances the learning process but also prepares students for the complexities of patient care in a technology-driven environment (Grunhut et al., 2022). Furthermore, the incorporation of AI into curricula can foster critical thinking and decision-making skills, essential for navigating the ethical dilemmas that arise in medical practice (Grunhut et al., 2022). Despite the promising applications of AI in education, the integration of these technologies into medical curricula has been slow. A scoping review highlighted that many medical schools have yet to adopt AI training, primarily due to a lack of systematic evidence supporting its implementation (Lee et al., 2021). Additionally, concerns regarding data protection and the ethical implications of AI use in healthcare education have been raised, indicating a need for comprehensive AI education that addresses these issues (Veras et al., 2023; Frehywot & Vovides, 2023). Students have expressed a desire for more robust training in AI, emphasizing the importance of understanding its role in healthcare delivery and decision-making processes (Ahmad et al., 2023; Derakhshanian et al., 2024). Moreover, the rapid advancement of AI technologies necessitates continuous curriculum updates to keep pace with emerging trends. As noted in recent literature, the integration of AI into biomedical science curricula should include subjects related to informatics, data sciences, and digital health (Sharma et al., 2024). This approach not only equips students with the necessary skills to utilize AI effectively but also prepares them for the evolving landscape of healthcare, where AI will play an integral role in diagnostics, treatment personalization, and patient management (Santos & Lopes, 2024; Secinaro et al., 2021). However, the implementation of AI in health sciences education is not without challenges. Ethical considerations surrounding AI's impact on healthcare equity and the potential for bias in AI algorithms must be addressed (Frehywot & Vovides, 2023; Han et al., 2019). Ensuring that AI technologies are used responsibly and equitably in education and practice is crucial to avoid exacerbating existing disparities in healthcare access and outcomes (Rigby, 2019). Furthermore, the lack of faculty expertise in AI poses a significant barrier to its integration into medical education, highlighting the need for targeted training and resources for educators (Derakhshanian et al., 2024). However, the integration of AI into health sciences education is not without challenges. Ethical concerns, such as data privacy and algorithmic bias, must be addressed to ensure that AI tools are used responsibly. Additionally, there is a risk of over-reliance on AI, potentially undermining the development of critical thinking and clinical judgment skills. Educators must strike a balance between leveraging AI’s capabilities and preserving the human elements of teaching and learning. Equity is another pressing issue. While AI has the potential to democratize education, access to these technologies remains uneven. Institutions in low-resource settings may struggle to adopt AI-driven tools, exacerbating existing disparities in global health education. Policymakers and educators must work together to ensure that the benefits of AI are accessible to all, regardless of geographic or socioeconomic barriers. In conclusion, AI is a powerful tool that holds immense promise for transforming health sciences education. By personalizing learning, enhancing clinical training, and supporting educators, AI can help prepare the next generation of healthcare professionals to meet the demands of an increasingly complex healthcare landscape. However, its integration must be guided by ethical principles and a commitment to equity, However, the successful integration of AI into educational curricula requires a concerted effort to address ethical concerns, update training programs, and equip both students and faculty with the necessary knowledge and skills. As the healthcare landscape continues to evolve, embracing AI in education will be essential for fostering a new generation of healthcare providers who are adept at leveraging technology to improve patient care. As we embrace this technological revolution, we must remember that AI is not a replacement for human expertise but a complement to it. The future of health sciences education lies in the synergy between human ingenuity and artificial intelligence.

Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.

Objective: The research aims to explore the integration of Artificial Intelligence (AI) within educational systems and analyze its impact on the governance of higher education institutions (HEIs), particularly focusing on decision-making, data protection, and administrative efficiency. Theoretical Framework: The article presents key theories on the transformative role of AI in educational governance, particularly focusing on how AI-driven data analysis and automation enhance decision-making and administrative efficiency. It also addresses theories related to ethical governance, emphasizing data protection and equitable access within higher education institutions. Method: The research methodology in this article is based on a qualitative approach, combining a review of existing literature with case studies of AI implementation in educational contexts. This approach provides in-depth insights into the effects of AI on governance within higher education institutions. Results and Discussion: The research findings highlight that AI integration in higher education governance improves decision-making and operational efficiency through data-driven insights and automation. However, it also reveals challenges, particularly in data protection, ethical concerns, and shifting power dynamics within institutions. The study emphasizes the need for responsible and transparent AI governance to ensure balanced benefits across stakeholders. Research Implications: This research underscores the need for higher education institutions to adopt AI responsibly, balancing its potential to enhance governance and decision-making with rigorous ethical standards, especially in data privacy and equity. It calls on policymakers and administrators to develop frameworks that ensure AI-driven processes remain transparent, inclusive, and aligned with educational values. Originality/Value: The originality of this research lies in its focus on how AI specifically transforms governance in higher education institutions, going beyond general applications of AI in education to address ethical, operational, and decision-making challenges unique to institutional governance. It provides a nuanced perspective on balancing innovation with responsibility in an academic setting.

The advancement of artificial intelligence (AI) technology has created both opportunities and risks in data protection and safeguarding. Given that numerous organizations are now employing AI systems in various industries, the goal of using data to drive innovation has never been urgent. This paper will review the relationship between AI, data privacy, and security and discuss the current issues and possible recommendations. Furthermore, this study introduces new approaches, including federated learning and homomorphic encryption, which preserve data integrity while still using the data. Using concrete examples from various industries, the study reveals practices and tendencies that company leaders should follow and avoid to achieve a proper balance. This paper offers an ethical approach that integrates practical recommendations for policymakers, technologists, and businesses to build user trust and progress responsibly and technically. Since most AI applications are based on big data, users’ data protection and systems’ performance and expandability are extremely important. This paper explores the issue of data privacy and security in AI and discusses promising strategies to address the problem, and guidelines for responsible AI implementation. The main priorities include the exposition of the algorithms, data anonymization methods, legal requirements, and strengthening cybersecurity. This paper presents real-life examples, and industry benchmarks to support the framework that can help organizations manage technologies in a way that addresses ethical concerns. In the future, the analysis presented in the study can help industries understand trends that help develop AI strategies that meet high privacy and security standards.

The advancement of artificial intelligence (AI) technology has created both opportunities and risks in data protection and safeguarding. Given that numerous organizations are now employing AI systems in various industries, the goal of using data to drive innovation has never been urgent. This paper will review the relationship between AI, data privacy, and security and discuss the current issues and possible recommendations. Furthermore, this study introduces new approaches, including federated learning and homomorphic encryption, which preserve data integrity while still using the data. Using concrete examples from various industries, the study reveals practices and tendencies that company leaders should follow and avoid to achieve a proper balance. This paper offers an ethical approach that integrates practical recommendations for policymakers, technologists, and businesses to build user trust and progress responsibly and technically. Since most AI applications are based on big data, users’ data protection and systems’ performance and expandability are extremely important. This paper explores the issue of data privacy and security in AI and discusses promising strategies to address the problem, and guidelines for responsible AI implementation. The main priorities include the exposition of the algorithms, data anonymization methods, legal requirements, and strengthening cybersecurity. This paper presents real-life examples, and industry benchmarks to support the framework that can help organizations manage technologies in a way that addresses ethical concerns. In the future, the analysis presented in the study can help industries understand trends that help develop AI strategies that meet high privacy and security standards.

Abstract: This chapter delves into the intricate web of legal and regulatory frameworks governing the deployment of Artificial Intelligence (AI) in cybersecurity. In an era where digital threats evolve with alarming speed and complexity, AI offers a beacon of hope for robust defense mechanisms. However, the integration of AI into cybersecurity strategies introduces a plethora of legal and ethical considerations that must be navigated with precision. From data protection and privacy concerns under regulations like GDPR and CCPA to the challenges of ensuring fairness, accountability, and transparency in AI algorithms, the chapter explores the multifaceted legal landscape that organizations must traverse. It further examines the role of international regulatory bodies in shaping AI use in cybersecurity, highlighting the importance of staying abreast of regulatory changes and adopting a proactive compliance stance. Through real-world case studies, the chapter sheds light on the legal challenges and successes experienced by organizations deploying AI in their cybersecurity efforts, offering insights into best practices for ethical AI deployment. The evolving nature of legal and regulatory frameworks is analyzed, with predictions on future trends that stakeholders must prepare for. The chapter concludes with a call to action for ongoing vigilance and adaptation, emphasizing the critical role of legal and ethical considerations in leveraging AI to combat cyber threats and fraud effectively. Keywords: AI in Cybersecurity,Legal Frameworks,Regulatory Compliance,Data Privacy,Ethical AI,Intellectual Property Rights,International Regulatory Bodies,Bias Mitigation,Transparency and Explainability,Cross-Border Data Transfers and Future Trends in AI Regulation.

This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.

Since the end of 2019, global healthcare systems have been dealing with the COVID-19 pandemic.In oncology, the biggest questions concern interaction of COVID-19 with pre-existing cancer disease and with systemic anticancer treatments. With regards to immunotherapy, there is uncertainty about its effect in the context of COVID-19 in terms of probability and course of viral infection.Herein, we retrospectively report data of patients with advanced cutaneous squamous cell carcinoma (cSCC) treated with immunotherapy at five Italian referral cancer centers during the pandemic. cSCC is a disease poorly represented in the literature, typically affecting fragile, elderly patients, with multiple comorbidities and often immunosuppressed. Overall, 54 patients were identified, most of them coming from Lombardy and Piedmont, the two regions hit hardest by COVID in Italy. In most cases, our choice was to continue treatment, reserving temporary interruptions only to patients considered particularly at risk for age and comorbidity. A total of 9% of patients developed new-onset symptoms or had chest radiological assessment potentially related to COVID-19. Nasopharyngeal swabs were collected in all suspicious cases and two hospitalized patients were found to be positive. In conclusion, the outbreak of COVID-19 is a major worldwide health concern. Our data indicate that COVID-19 mortality in patients with cancer may be principally driven by advancing age, the presence of other comorbidities, and other cancer-related conditions (i.e. hospitalization). Our data further suggests the safety of continued use of PD-1 blockade during the COVID-19 pandemic (obviously implementing all the safety measures in the hospital environment) also considering the possible negative effects of a prolonged suspension on the course of the tumor evolution. We think it is useful to collect and report case studies coming from reference centers, because they can represent helpful examples for the scientific community of clinical management of patients affected by cancer in this difficult period and guide further research.

Artificial Intelligence (AI) is increasingly taking hold throughout worldwide businesses. Since 2017, anticipated AI usage in Malaysia has increased by 32 percent due to innovative city projects and public safety and intelligent transportation applications. However, such AI development is afflicted by privacy and data breaches as the existing data protection regulation safeguards against unlawful commercial use of personal data. Recent developments reflect the failure of present legal mechanisms to address such infringements and effective data management and protection deficiencies. This study examines the legal concerns of privacy and data protection and problems associated with the use of AI in Malaysia. This research adopts a methodology of doctrinal research, a systematic method of legal reasoning, including analyzing primary and secondary resources. The findings imply that AI evolves, magnifying the ability to use personal information that may affect privacy. Thus, infringement of privacy and use of personal data without legal oversight or effective governance could result in privacy deprivation and abuse of personal data that might be used to perpetrate crimes

In Europe, despite recent advances in clinical development, most of the drugs currently used to treat childhood cancers are adult medicines, prescribed outside of the authorized indication. In this context, a monocentric retrospective cohort analysis was conducted, evaluating pediatric, adolescent, and young adult patients affected by onco-hematologic disease, treated with targeted therapies used off-label or as compassionate use. The analysis was conducted on 45 patients aged less than or equal to 30years with cancer, having received at least one targeted therapy prescribed as off-label or compassionate use at a large Italian pediatric center between January 1, 2016 and June 30, 2021. Data collected included information on the patient and tumor, data on off-label/compassionate treatment, and data on safety and efficacy. Total 25 out of 45 patients treated with off-label or compassionate targeted therapies were affected by onco-hematological diseases. Overall, 22 out of the 52 agents (42%) were prescribed in patients with relapsed neoplasm and 39% (20/52) in patients with refractory diseases. Complete response was observed in more than half (27/52) of treatments. At least one adverse reaction occurred in 76% (n=22) of agents administered to patients with onco-hematological tumor and in 43% (n=10) of agents prescribed to patients with solid tumor. This work aims to provide a snapshot of off-label and compassionate use prescriptions in a large Italian pediatric cancer center. This study confirms that targeted agents for unauthorized indications are often prescribed in pediatric patients with cancer, especially after disease relapse and that these treatments are mostly tolerable and effective.

PurposeCost evaluation is becoming mandatory to support healthcare sustainability and optimize the decision-making process. This topic is a challenge, especially for complex and rapidly evolving treatment modalities such as radiotherapy (RT). The aim of the present study was to investigate the cost of RT in the last month of life of patients in an Italian cancer center.MethodsThis was a retrospective study on a cancer population (N= 160) who underwent RT or only an RT planning simulation in an end of life (EOL) setting. The cost of RT procedures performed on patients was collected according to treatment status, care setting, and RT technique used. Costs were valued according to the sum of reimbursements relating to all procedures performed and assessed from the perspective of the National Health System.ResultsThe total cost of RT in the last month of life was €244,774, with an average cost per patient of €1530. Around 7.7% and 30.3% of the total cost was associated with patients who never started RT or who discontinued RT, respectively, while the remaining 62.0% referred to patients who completed treatment. Costs associated with outpatient and inpatient settings represented 54.3% and 38.6% of the total cost, respectively. The higher average cost per patient for the never-started and discontinued groups was correlated with patients who had a poor ECOG Performance Status.ConclusionImproved prognostic accuracy and a better integration between radiotherapy and palliative care units could be a key to a better use of resources and to a reduction in the cost of EOL RT.

Social media networks are extensively using artificial intelligence tools (AI) fed with huge data about users’ preferences, mental state, mood, health, and other. The potential risk of manipulating this “Big Data” to predict users ‘behavior, customize users’ profiles and create a “Virtual social world” for each one is of major importance. Using algorithms to make sense of streams of data, known as the discipline of data analytics, and how it’s applied in social media platforms and decision-making, raises ethical concerns about data privacy and data protection. This research is conducted to answer questions related to users’ perceptions of privacy, risk of sharing personal data on social media and to what extent are people aware of the use of AI and the risks they face while using SNSs, as well as the ethical issues related to the use of artificial intelligence in social media. This study focuses on measuring people’s perceptions of privacy through four main variables and their interrelation. The results emphasized that people perceived themselves at risk on social media, they have concerns about their data being manipulated and misused, this affects their overall perception of privacy, the sample consisted of 200 respondents through an online survey. People’s perceptions of privacy could be an indicator of how they will act regarding the sharing of personal information and feelings on social media.