Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact

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

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Integrative analysis of AI-driven optimization in HIV treatment regimens

Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(6), С. 1314 - 1334

Опубликована: Июнь 7, 2024

The integration of artificial intelligence (AI) into HIV treatment regimens has revolutionized the approach to personalized care and optimization strategies. This study presents an in-depth analysis role AI in transforming treatment, focusing on its ability tailor therapy individual patient needs enhance outcomes. AI-driven involves utilization advanced algorithms computational techniques analyze vast amounts data, including genetic information, viral load measurements, history. By harnessing power machine learning predictive analytics, can identify patterns trends data that may not be readily apparent human clinicians. One key benefits is personalize based characteristics disease progression. considering factors such as drug resistance profiles, comorbidities, lifestyle factors, recommend most effective well-tolerated options for each patient, leading improved adherence clinical Furthermore, enables continuous monitoring adjustment real time, allowing healthcare providers respond rapidly changes status evolving dynamics. proactive management help prevent failure development resistance, ultimately better long-term outcomes patients. Despite transformative potential, without challenges. Ethical considerations, privacy concerns, need robust validation regulatory oversight are all important must addressed ensure safe implementation practice. In conclusion, integrative presented this underscores significant impact personalization regimens. leveraging technologies, approaches needs, quality life people living with HIV. Keywords: Integrative Analysis, AI- Driven, Optimization, Treatment, Regimens.

Язык: Английский

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Innovations in real-time infectious disease surveillance using AI and mobile data

International Medical Science Research Journal, Год журнала: 2024, Номер 4(6), С. 647 - 667

Опубликована: Июнь 6, 2024

The integration of artificial intelligence (AI) and mobile health data has ushered in a new era real-time infectious disease surveillance, offering unprecedented insights into dynamics enabling proactive public interventions. This paper explores the innovative applications AI transforming traditional surveillance systems for diseases. By harnessing power algorithms, coupled with vast amount generated from devices, researchers authorities can now monitor outbreaks greater accuracy efficiency. AI-driven predictive models analyze diverse datasets, including demographic information, travel patterns, social media activity, to detect early signs emergence predict potential outbreaks. use provides wealth information that was previously inaccessible methods. Mobile apps, wearables, other connected devices enable continuous monitoring individuals' indicators, allowing detection symptoms rapid response threats. Furthermore, geolocation facilitates tracking population movements identification high-risk areas transmission. However, this approach also presents challenges ethical considerations. Privacy concerns regarding collection must be carefully addressed ensure rights are protected. Additionally, issues related quality, interoperability, algorithm bias need mitigated reliability effectiveness systems. In conclusion, holds immense promise revolutionizing surveillance. leveraging these technologies, gain valuable dynamics, enhance capabilities, implement targeted interventions prevent spread it is essential address considerations associated its responsible effective implementation. Keywords: Innovations, Real-Time Infectious Disease, Surveillance, AI, Data.

Язык: Английский

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PUBLIC-PRIVATE PARTNERSHIPS IN HEALTH SECTOR INNOVATION: LESSONS FROM AROUND THE WORLD

International Medical Science Research Journal, Год журнала: 2024, Номер 4(4), С. 484 - 499

Опубликована: Апрель 20, 2024

Public-Private Partnerships (PPPs) have emerged as a crucial mechanism for fostering innovation in the health sector globally. This review encapsulates lessons learned from diverse PPP models worldwide, highlighting their significance and impact. PPPs healthcare entail collaboration between governmental bodies, private enterprises, sometimes non-profit organizations to address challenges, such limited resources, expertise, infrastructure, while leveraging strengths of each sector. The success relies on effective governance structures, clear objectives, mutual accountability. One notable example is United Kingdom's NHS Innovation Accelerator, which partners with industry leaders fast-track adoption innovative technologies within National Health Service (NHS). Through this initiative, pioneering solutions, ranging digital platforms medical devices, been implemented, enhancing patient care operational efficiency. Similarly, low-resource settings like sub-Saharan Africa, played pivotal role improving access essential services. Projects Medicines Malaria Venture (MMV) collaborate pharmaceutical companies, governments, research institutions develop affordable antimalarial drugs tailored region's needs. In realm development, partnerships Coalition Epidemic Preparedness Innovations (CEPI) demonstrated power international addressing global threats. CEPI brings together philanthropic organizations, expedite development vaccines against emerging infectious diseases, witnessed during COVID-19 pandemic. However, challenges persist implementation, including complex regulatory frameworks, funding uncertainties, divergent interests among stakeholders. Lessons successful underscore importance transparent communication, stakeholder engagement, sustained political commitment. conclusion, represent dynamic avenue catalyzing driving transformative change. By drawing insights experiences policymakers practitioners can refine existing frameworks foster sustainable tackle evolving effectively. Keywords: Partnership, Health, Innovation, World, Review.

Язык: Английский

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A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records

BMC Medical Informatics and Decision Making, Год журнала: 2025, Номер 25(1)

Опубликована: Фев. 17, 2025

The clinical information housed within unstructured electronic health records (EHRs) has the potential to promote cancer research. National Cancer Center Hospital (NCCH) is widely recognized as a leading institution for treatment of thoracic malignancies in Japan. Information on medical treatment, particularly characteristics malignant tumors that occur patients, tumor response evaluation, and adverse events, was compiled into databases each NCCH department from EHRs. However, there have been few opportunities integrated analysis data both hospital research institute. We developed method predicting evaluation survival curves drug therapy EHRs lung patients using natural language processing. First, we rule-based algorithm predict duration dictionary anticancer drugs regimens used treatment. Thereafter, applied supervised learning radiology reports during period constructed classification model date when progressive disease (PD) determined. predicted PD can be draw curve progression-free survival. 716 treatments at structured cases labels training testing learning. were manually curated by physicians CRCs. investigated results performance proposed method. Individual predictions not extremely high. final nearly similar actual curves. Although it difficult construct fully automated system our method, believe achieves sufficient supporting CRCs constructing database providing help researchers find out chance studies.

Язык: Английский

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Smart Solutions for Health

Advances in medical technologies and clinical practice book series, Год журнала: 2024, Номер unknown, С. 194 - 211

Опубликована: Апрель 26, 2024

This study investigates the impact of computational intelligence on healthcare sector within context Industry 4.0 paradigm. Healthcare providers can improve patient care, operational efficiency, and cost-effectiveness by utilizing technology such as machine learning artificial intelligence. also examines several applications, including disease detection, tailored treatment planning, predictive modeling, resource management. Valuable insights are derived from medical data through utilization big analysis advanced algorithms, resulting in enhanced diagnosis, optimal therapies, preventive interventions. Smart systems provide continuous monitoring patients, timely identification potential risks, provision care.

Язык: Английский

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Artificial Intelligence (AI) and Machine Learning (ML) Implemented Drug Delivery Systems: A paradigm shift in the Pharmaceutical industry

Journal of Bio-X Research, Год журнала: 2024, Номер 7

Опубликована: Янв. 1, 2024

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the pharmaceutical industry, particularly in drug development delivery. These technologies enable precision medicine by analyzing extensive datasets to optimize formulations predict patient responses. AI-driven models enhance nanoparticle-based carriers, improving their stability, bioavailability, targeting accuracy. ML also facilitates real-time monitoring adaptive control of release, ensuring better therapeutic outcomes. This review explores integration AI delivery, highlighting potential accelerate development, reduce costs, advance personalized medicine.

Язык: Английский

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Developing predictive models for HIV Drug resistance: A genomic and AI approach

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 521 - 543

Опубликована: Май 5, 2024

This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With increasing prevalence drug-resistant strains HIV, there is critical need for innovative strategies predict manage mutations, thereby optimizing treatment outcomes prolonging efficacy antiretroviral therapy (ART). Drawing on advances in genomics AI, this study outlines conceptual framework that can identify potential drug-resistance mutations genomes inform clinical decision-making. The proposed integrates from HIV-infected individuals with AI algorithms capable learning complex patterns within data. By analyzing sequences obtained HIV-positive patients, aim genetic variations associated resistance, likelihood development, guide selection appropriate regimens. holds promise personalized medicine care, enabling clinicians tailor based an individual's profile risk resistance. Key components include preprocessing extract relevant features, model training using machine techniques such as deep ensemble methods, validation performance cross-validation independent testing. Furthermore, integration data, history viral load measurements, enhances accuracy provides valuable insights into response dynamics.The represents paradigm shift offering proactive management surveillance. technologies, healthcare providers anticipate address emerging before they compromise efficacy. Ultimately, implementation improve patient outcomes, reduce transmission strains, advance global fight against HIV/AIDS. Keywords: Developing, Predictive Models, Drug Resistance, Genomic, Approach.

Язык: Английский

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Ethical Implications in AI-Based Health Care Decision Making: A Critical Analysis

AI in Precision Oncology, Год журнала: 2024, Номер 1(5), С. 246 - 255

Опубликована: Июль 4, 2024

Язык: Английский

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Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

International Medical Science Research Journal, Год журнала: 2024, Номер 4(5), С. 558 - 578

Опубликована: Май 5, 2024

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains significant challenge. This review explores effectiveness challenges of using machine learning (ML) for forecasting spread high-risk areas. ML models have shown promise identifying patterns trends data, enabling more accurate predictions targeted interventions. insights into outbreak leverage various data sources, including demographic, epidemiological, behavioural data. By analysing these algorithms can identify populations geographical areas susceptible to transmission. information is crucial public health authorities allocate resources efficiently implement preventive measures effectively. Despite potential benefits, several exist predictions. These include quality issues, such as incomplete or inaccurate which affect reliability Additionally, complexity transmission dynamics need real-time pose models. To address challenges, researchers practitioners are exploring innovative approaches, integrating multiple sources advanced techniques. Collaborations between researchers, officials, technology experts also developing robust In conclusion, while offers valuable addressing model essential its effective use. overcoming has significantly improve prevention efforts ultimately reduce burden region. Keywords: Machine Learning, AI, Outbreaks: Predictions, Insights.

Язык: Английский

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