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Enregistrement W6962759173 · doi:10.17605/osf.io/vghr3

Healing at a Distance: Telemedicine and Remote Care in the Age of Artificial Intelligence

2024· article· en· W6962759173 sur OpenAlexaboutno aff

Notice bibliographique

RevueOSF Preprints (OSF Preprints) · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueTelemedicine and Telehealth Implementation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTelemedicineHealth careThe InternetTelehealthPandemiceHealth

Résumé

récupéré en direct d'OpenAlex

Rachmad, Yoesoep Edhie. 2021. Healing at a Distance: Telemedicine and Remote Care in the Age of Artificial Intelligence. Book of Medical Internet Research; Toronto Special Issue, 2021. https://doi.org/10.17605/osf.io/vghr3 "Healing at a Distance: Telemedicine and Remote Care in the Age of Artificial Intelligence" by Yoesoep Edhie Rachmad, published in 2021 by the Book of Medical Internet Research in Toronto, explores the transformative impact of telemedicine and AI on healthcare delivery. The book addresses the increasing need for accessible and high-quality healthcare solutions, especially in remote and underserved areas. It provides a comprehensive overview of how telemedicine has evolved and the pivotal role AI plays in enhancing remote care. Definition and Basic Concepts The book begins with an introduction to telemedicine and remote care, defining these concepts and tracing their development over the years. Telemedicine refers to the use of telecommunication technology to provide healthcare services from a distance, while remote care encompasses a broader spectrum of health services delivered outside traditional healthcare settings. AI is presented as a key enabler, enhancing the capabilities of telemedicine through advanced algorithms and data analysis. Underlying Phenomena The motivation for this book stems from the rapid advancements in telecommunication and AI technologies, coupled with the growing demand for healthcare accessibility. The COVID-19 pandemic has further accelerated the adoption of telemedicine, highlighting its importance in ensuring continuity of care. The book emphasizes how these technological advancements can address critical healthcare challenges and improve patient outcomes. Problem Statement The central problem addressed by the book is the integration of AI into telemedicine and remote care. It examines the challenges and opportunities of incorporating AI technologies in telemedicine, aiming to understand how these technologies can be effectively utilized to expand access, improve quality, and ensure sustainability of healthcare services. Research Objectives The book aims to explore the various applications of AI in telemedicine, providing a comprehensive analysis of their benefits, challenges, and future potential. It seeks to offer insights into how these technologies can be implemented to enhance remote care, emphasizing the importance of ethical guidelines and regulatory frameworks to ensure responsible use. Indicators Key indicators of successful AI integration in telemedicine, as identified in the book, include improved patient triage, accurate initial diagnoses, efficient case management, and enhanced patient engagement. The book also highlights the importance of robust technological infrastructure and supportive regulatory policies as critical indicators. Operational Variables Operational variables discussed in the book include AI technologies such as machine learning algorithms, natural language processing tools, and IoT devices. The book also explores variables related to patient outcomes, data management practices, and regulatory compliance. Determining Factors Several factors are crucial for the successful implementation of AI in telemedicine, including technological advancements, healthcare professionals' readiness to adopt new tools, regulatory support, and patient acceptance. The author emphasizes the role of interdisciplinary collaboration and continuous innovation in overcoming technical and ethical challenges. Implementation and Strategy The book outlines various strategies for integrating AI into telemedicine, such as investing in AI research and development, fostering collaboration between technology developers and healthcare providers, and establishing comprehensive training programs for healthcare workers. It also highlights the need for continuous monitoring and evaluation to adapt to evolving technologies and healthcare needs. Challenges and Supportive Factors The book identifies several challenges, including data privacy concerns, algorithmic biases, and the complexity of cross-jurisdictional medical licensing. Supportive factors include ongoing technological innovations, supportive regulatory policies, and positive patient outcomes. The author calls for a balanced approach to address these challenges while leveraging supportive factors to maximize the benefits of AI in telemedicine. Determining Factors of the Book The relevance and impact of the book are determined by its timely exploration of emerging technologies, its comprehensive analysis, and its practical recommendations for healthcare professionals and policymakers. The book’s ability to address ethical considerations and propose actionable strategies also contributes significantly to its importance. Research Findings The book presents several case studies demonstrating successful applications of telemedicine and AI in various healthcare settings. These include efficient patient triage systems, remote diagnostic tools, and effective chronic disease management programs. These findings illustrate the tangible benefits of AI integration, providing evidence of its potential to transform remote healthcare delivery. Conclusion and Recommendations In conclusion, the book emphasizes the vital role of telemedicine and AI in modernizing healthcare. It advocates for the ethical and responsible adoption of these technologies, emphasizing the need for regulatory frameworks and continuous evaluation. The author recommends fostering interdisciplinary collaborations, investing in technological innovations, and developing comprehensive regulatory frameworks to ensure the successful integration of AI in telemedicine. "Healing at a Distance: Telemedicine and Remote Care in the Age of AI" offers a detailed exploration of how telemedicine and AI can overcome geographical and resource barriers, enhancing the quality and effectiveness of healthcare. It underscores the importance of innovation, ethical responsibility, and strategic implementation to harness the full potential of these transformative technologies. Buku: "Healing at a Distance: Telemedicine and Remote Care in the Age of AI" Bab 1: Pengantar ke Telemedisin dan Perawatan Jarak Jauh • Isi: Bab ini memberikan gambaran umum tentang evolusi telemedisin dan perawatan jarak jauh, menyoroti bagaimana teknologi, terutama AI, telah memungkinkan perkembangan pesat dalam bidang ini. • Kesimpulan: Telemedisin telah berkembang menjadi solusi kesehatan kritis yang memperluas akses dan meningkatkan kualitas perawatan, terutama di area terpencil. Bab 2: AI dalam Telemedisin • Isi: Menganalisis peran AI dalam meningkatkan layanan telemedisin melalui algoritma yang dapat melakukan triase pasien, diagnosa awal, dan manajemen kasus. • Kesimpulan: Penggunaan AI dalam telemedisin menawarkan potensi untuk membuat layanan kesehatan lebih efisien dan dapat diakses oleh lebih banyak orang. Bab 3: Teknologi Pendukung Telemedisin • Isi: Mendiskusikan berbagai teknologi yang mendukung telemedisin, termasuk platform komunikasi, perangkat IoT kesehatan, dan sistem manajemen data. • Kesimpulan: Infrastruktur teknologi yang solid adalah kunci untuk menyediakan layanan telemedisin yang aman, efektif, dan berkelanjutan. Bab 4: Pengaruh Telemedisin pada Perawatan Primer • Isi: Mengeksplorasi dampak telemedisin pada perawatan primer, bagaimana itu mengubah interaksi dokter-pasien dan pengelolaan penyakit kronis. • Kesimpulan: Telemedisin telah menjadi alat penting dalam perawatan primer, meningkatkan monitoring terus-menerus dan pendekatan perawatan preventif. Bab 5: Isu Hukum dan Regulasi • Isi: Membahas tantangan hukum dan regulasi yang dihadapi oleh penyedia telemedisin, termasuk privasi data, keamanan, dan lintas yurisdiksi perizinan medis. • Kesimpulan: Memahami dan mengatasi tantangan regulasi adalah esensial untuk integrasi yang sukses dan etis dari telemedisin dalam sistem kesehatan. Bab 6: Studi Kasus Global • Isi: Menampilkan berbagai studi kasus dari seluruh dunia yang menunjukkan penerapan efektif dan inovatif dari telemedisin dan perawatan jarak jauh. • Kesimpulan: Studi kasus ini menyoroti keberhasilan dan tantangan telemedisin, memberikan wawasan penting untuk pengembangan masa depan. Bab 7: Masa Depan Telemedisin • Isi: Meninjau perkiraan masa depan telemedisin, termasuk peran potensial teknologi baru seperti AI lanjutan, realitas virtual, dan lebih lagi. • Kesimpulan: Masa depan telemedisin dipenuhi dengan peluang untuk inovasi lebih lanjut yang akan terus mengubah cara layanan kesehatan disediakan. Kesimpulan Akhir: • Isi: Bab ini mengintegrasikan semua poin kunci dari bab-bab sebelumnya, menegaskan kembali pentingnya telemedisin dalam masyarakat modern dan bagaimana AI berpotensi memperluas kemampuannya. • Kesimpulan: Seiring berkembangnya teknologi, telemedisin akan terus memainkan peran penting dalam menyediakan akses perawatan kesehatan yang inklusif dan berkelanjutan. Buku ini menyediakan pandangan mendalam dan terperinci tentang bagaimana telemedisin dan AI mengubah wajah perawatan kesehatan, mengatasi hambatan geografis dan sumber daya, serta meningkatkan kualitas dan efektivitas perawatan.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,902
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0230,009

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,350
Écart entre enseignants0,317 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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