A Randomized Non‐overlapping Encryption Scheme for Enhanced Image Security in Internet of Things (IoT) Applications DOI Creative Commons
Muhammad Aqeel, Arfan Jaffar,

Muhammad Faheem

и другие.

Engineering Reports, Год журнала: 2025, Номер 7(1)

Опубликована: Янв. 1, 2025

ABSTRACT The rapid proliferation of Internet Things (IoT) devices has underscored the critical need to safeguard data they store and transmit. Among various types, digital images often carry highly sensitive information, making their protection against breaches essential. This study introduces a novel image encryption algorithm specifically designed bolster security in resource‐constrained IoT ecosystems. Leveraging randomness 5D multi‐wing hyperchaotic map, proposed method employs pairs non‐overlapping rectangles induce confusion by swapping pixels encompass. Repeated iterations this operation achieve significant effects, enhancing strength. To validate robustness algorithm, standard benchmark were utilized, rigorous metrics —including information entropy, correlation coefficient, histogram uniformity, resistance differential attacks —were analyzed. Results demonstrate that not only ensures strong unauthorized access but also maintains low computational complexity, it ideal for applications. research provides foundational step toward ensuring confidentiality integrity visual an increasingly interconnected world.

Язык: Английский

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice DOI
Rohan Khera, Evangelos K. Oikonomou, Girish N. Nadkarni

и другие.

Journal of the American College of Cardiology, Год журнала: 2024, Номер 84(1), С. 97 - 114

Опубликована: Июнь 24, 2024

Язык: Английский

Процитировано

44
AI'S IMPACT ON PERSONALIZED MEDICINE: TAILORING TREATMENTS FOR IMPROVED HEALTH OUTCOMES DOI Creative Commons

Francisca Chibugo Udegbe,

Ogochukwu Roseline Ebulue,

Charles Chukwudalu Ebulue

и другие.

Engineering Science & Technology Journal, Год журнала: 2024, Номер 5(4), С. 1386 - 1394

Опубликована: Апрель 17, 2024

This review paper explores the transformative impact of artificial intelligence (AI) on personalized medicine and its potential to revolutionize healthcare outcomes. AI technologies, ranging from data analysis interpretation diagnostic tools treatment planning, offer unprecedented opportunities for tailoring medical interventions individual patient characteristics. Through sophisticated algorithms, facilitates complex biological data, predicts disease risks, enhances accuracy. Furthermore, AI-powered promises expand access high-quality address global health disparities. However, challenges such as privacy, bias, regulatory hurdles must be addressed ensure responsible integration into practices. underscores importance interdisciplinary collaboration, ethical considerations, policy-making efforts in harnessing AI's advance responsibly. Keywords: Artificial Intelligence, Personalized Medicine, Healthcare, Data Analysis, Ethical Considerations, Interdisciplinary Collaboration

Язык: Английский

Процитировано

40
A future role for health applications of large language models depends on regulators enforcing safety standards DOI Creative Commons
Oscar Freyer, Isabella C. Wiest, Jakob Nikolas Kather

и другие.

The Lancet Digital Health, Год журнала: 2024, Номер 6(9), С. e662 - e672

Опубликована: Авг. 23, 2024

Among the rapid integration of artificial intelligence in clinical settings, large language models (LLMs), such as Generative Pre-trained Transformer-4, have emerged multifaceted tools that potential for health-care delivery, diagnosis, and patient care. However, deployment LLMs raises substantial regulatory safety concerns. Due to their high output variability, poor inherent explainability, risk so-called AI hallucinations, LLM-based applications serve a medical purpose face challenges approval devices under US EU laws, including recently passed Artificial Intelligence Act. Despite unaddressed risks patients, misdiagnosis unverified advice, are available on market. The ambiguity surrounding these creates an urgent need frameworks accommodate unique capabilities limitations. Alongside development frameworks, existing regulations should be enforced. If regulators fear enforcing market dominated by supply or technology companies, consequences layperson harm will force belated action, damaging potentiality advice.

Язык: Английский

Процитировано

26
Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact DOI Creative Commons
Hamid Reza Saeidnia,

Seyed Ghasem Hashemi Fotami,

Brady Lund

и другие.

Social Sciences, Год журнала: 2024, Номер 13(7), С. 381 - 381

Опубликована: Июль 22, 2024

AI has the potential to revolutionize mental health services by providing personalized support and improving accessibility. However, it is crucial address ethical concerns ensure responsible beneficial outcomes for individuals. This systematic review examines considerations surrounding implementation impact of artificial intelligence (AI) interventions in field well-being. To a comprehensive analysis, we employed structured search strategy across top academic databases, including PubMed, PsycINFO, Web Science, Scopus. The scope encompassed articles published from 2014 2024, resulting 51 relevant articles. identifies 18 key considerations, 6 associated with using wellbeing (privacy confidentiality, informed consent, bias fairness, transparency accountability, autonomy human agency, safety efficacy); 5 principles development technologies settings practice positive (ethical framework, stakeholder engagement, review, mitigation, continuous evaluation improvement); 7 practices, guidelines, recommendations promoting use (adhere transparency, prioritize data privacy security, mitigate involve stakeholders, conduct regular reviews, monitor evaluate outcomes). highlights importance By addressing privacy, bias, oversight, evaluation, can that like chatbots AI-enabled medical devices are developed deployed an ethically sound manner, respecting individual rights, maximizing benefits while minimizing harm.

Язык: Английский

Процитировано

22
Advancing Chinese biomedical text mining with community challenges DOI Creative Commons
Hui Zong, Rongrong Wu,

Jiaxue Cha

и другие.

Journal of Biomedical Informatics, Год журнала: 2024, Номер 157, С. 104716 - 104716

Опубликована: Авг. 27, 2024

Objective: This study aims to review the recent advances in community challenges for biomedical text mining China.Methods: We collected information of evaluation tasks released mining, including task description, dataset data source, type and related links.A systematic summary comparative analysis were conducted on various natural language processing tasks, such as named entity recognition, normalization, attribute extraction, relation event classification, similarity, knowledge graph construction, question answering, generation, large model evaluation.Results: identified 39 from 6 that spanned 2017 2023.Our revealed diverse range types sources mining.We explored potential clinical applications these challenge a translational informatics perspective.We compared with their English counterparts, discussed contributions, limitations, lessons guidelines challenges, while highlighting future directions era models.Conclusion: Community competitions have played crucial role promoting technology innovation fostering interdisciplinary collaboration field mining.These provide valuable platforms researchers develop state-of-the-art solutions.

Язык: Английский

Процитировано

17
Current Practices and Perspectives of Artificial Intelligence in the Clinical Management of Eating Disorders: Insights From Clinicians and Community Participants DOI Creative Commons
Jake Linardon, Claudia Liu, Mariel Messer

и другие.

International Journal of Eating Disorders, Год журнала: 2025, Номер unknown

Опубликована: Янв. 19, 2025

ABSTRACT Objective Artificial intelligence (AI) could revolutionize the delivery of mental health care, helping to streamline clinician workflows and assist with diagnostic treatment decisions. Yet, before AI can be integrated into practice, it is necessary understand perspectives these tools inform facilitators barriers their uptake. We gathered data on community participant incorporating in clinical management eating disorders. Method A survey was distributed internationally clinicians ( n = 116) experience disorder (psychologists, psychiatrists, etc.) participants 155) who reported occurrence behaviors. Results 59% use systems (most commonly ChatGPT) for professional reasons, compared 18% using them help‐related purposes. While more than half (58%) (53%) were open help support them, fewer enthusiastic about integration (40% 27%, respectively) believed that they would significantly improve client outcomes (28% 13%, respectively). Nine 10 agreed may improperly used if individuals are not adequately trained, pose new privacy security concerns. Most will convenient, beneficial administrative tasks, an avenue continuous support, but never outperform human relational skills. Conclusion many recognize its possible wide‐ranging benefits, most remain cautious uncertain implementation.

Язык: Английский

Процитировано

7
Prospective Applications of Artificial Intelligence In Fetal Medicine: A Scoping Review of Recent Updates DOI Creative Commons
Elhadi Miskeen, Jaber Alfaifi,

Dalal Mohammed Alhuian

и другие.

International Journal of General Medicine, Год журнала: 2025, Номер Volume 18, С. 237 - 245

Опубликована: Янв. 1, 2025

With the incorporation of artificial intelligence (AI), significant advancements have occurred in field fetal medicine, holding potential to transform prenatal care and diagnostics, promising revolutionize diagnostics. This scoping review aims explore recent updates prospective application AI evaluating its current uses, benefits, limitations. Compiling literature concerning utilization medicine does not appear modify subject or provide an exhaustive exploration electronic databases. Relevant studies, reviews, articles published years were incorporated ensure up-to-date data. The selected works analyzed for common themes, methodologies applied, scope AI's integration into practice. identified several key areas where applications are making strides including screening, diagnosis congenital anomalies, predicting pregnancy complications. AI-driven algorithms been developed analyze complex ultrasound data, enhancing image quality interpretative accuracy. monitoring has also explored, with systems designed identify patterns indicative distress. Despite these advancements, challenges related ethical use AI, data privacy, need extensive validation tools diverse populations noted. benefits immense, offering a brighter future our field. equips us enhanced diagnosis, monitoring, prognostic capabilities, way we approach optimistic outlook underscores further research interdisciplinary partnerships fully leverage driving forward practice medicine.

Язык: Английский

Процитировано

3
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection (Preprint) DOI Creative Commons

Mahesh Vaijainthymala Krishnamoorthy

JMIRx Med, Год журнала: 2025, Номер 6, С. e70100 - e70100

Опубликована: Фев. 2, 2025

Abstract Background The increasing integration of artificial intelligence (AI) systems into critical societal sectors has created an urgent demand for robust privacy-preserving methods. Traditional approaches such as differential privacy and homomorphic encryption often struggle to maintain effective balance between protecting sensitive information preserving data utility AI applications. This challenge become particularly acute organizations must comply with evolving governance frameworks while maintaining the effectiveness their systems. Objective paper aims introduce validate obfuscation through latent space projection (LSP), a novel technique designed enhance ensure responsible compliance. primary goal is develop method that can effectively protect essential features necessary model training inference, thereby addressing limitations existing approaches. Methods We developed LSP using combination advanced machine learning techniques, specifically leveraging autoencoder architectures adversarial training. projects lower-dimensional space, where it separates from nonsensitive information. separation enables precise control over privacy-utility trade-offs. validated comprehensive experiments on benchmark datasets implemented 2 real-world case studies: health care application focusing cancer diagnosis financial services analyzing fraud detection. Results demonstrated superior performance across multiple evaluation metrics. In image classification tasks, achieved 98.7% accuracy strong protection, providing 97.3% against attribute inference attacks. significantly exceeded traditional anonymization studies further LSP’s effectiveness, showing in both Additionally, alignment global frameworks, including General Data Protection Regulation, California Consumer Privacy Act, Health Insurance Portability Accountability Act. Conclusions represents significant advancement AI, offering promising approach developing respect individual delivering valuable insights. By embedding protection directly within pipeline, contributes key principles fairness, transparency, accountability. Future research directions include theoretical guarantees, exploring federated systems, enhancing interpretability. These developments position crucial tool advancing ethical practices ensuring technology deployment privacy-sensitive domains.

Язык: Английский

Процитировано

3
Applying Large Language Model (LLM) for Developing Cybersecurity Policies to Counteract Spear Phishing Attacks on Senior Corporate Managers DOI Creative Commons
Thomas Quinn, Olivia Thompson

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Май 14, 2024

Abstract Applying Google Gemini's generative AI capabilities, this research provided a novel approach to developing and implementing cybersecurity policies targeted at mitigating spear phishing attacks against senior corporate managers. The study demonstrated significant enhancements in the detection, prevention, response strategies within frameworks, by integrating advanced artificial intelligence with traditional security protocols. application of machine learning algorithms not only improved accuracy speed threat detection but also enabled dynamic policy adjustments based on real-time data analysis, proving crucial evolving landscape digital threats. findings underscore potential transform practices, offering more adaptable, proactive, robust defenses increasingly sophisticated techniques. Further, explores implications AI-driven for governance compliance, suggesting new paradigm which supports actively defines strategic decisions. promising results invite further investigation into broader applications cybersecurity, pointing toward future where integration is standard defense complex cyber

Язык: Английский

Процитировано

13
Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications DOI Creative Commons
Tala Mirzaei, Leila Amini, Pouyan Esmaeilzadeh

и другие.

BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)

Опубликована: Сен. 9, 2024

Язык: Английский

Процитировано

9