From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery
<p>Artificial intelligence (AI) is now woven into the everyday clinical practice of internal medicine, neurology, neurosurgery, and dermatology, transforming how clinicians triage, diagnose, document, and operate. From the early rule-based systems of the 1970s to today’s deep-learning platform...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
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| Acceso en línea: | https://www.intechopen.com/chapters/1237227 |
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| _version_ | 1879164891767504896 |
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| author | Alrefaie, Khadeja Madlol, Basel Atallah, Oday |
| author_browse | Alrefaie, Khadeja Atallah, Oday Madlol, Basel |
| author_facet | Alrefaie, Khadeja Madlol, Basel Atallah, Oday |
| author_sort | Alrefaie, Khadeja |
| building | IntechOpen |
| collection | Acceso abierto |
| description | <p>Artificial intelligence (AI) is now woven into the everyday clinical practice of internal medicine, neurology, neurosurgery, and dermatology, transforming how clinicians triage, diagnose, document, and operate. From the early rule-based systems of the 1970s to today’s deep-learning platforms and multimodal foundation models, the evolution of medical AI reflects simultaneous advances in data availability, computational power, and regulatory oversight. Contemporary tools – automated stroke triage, rapid-response EEG, seizure-detection wearables, dermatologic image classifiers, and augmented-reality surgical navigation – have accelerated clinical workflows while raising new questions about validation, safety, and equity. This chapter traces that trajectory and examines the distinction between regulatory clearance, technical accuracy, and true clinical benefit. It outlines evidence standards across specialties, highlights lessons from successful and unsuccessful deployments, and provides a practical governance and education framework for integrating AI responsibly into training programs. As AI moves from proof-of-concept to routine care, medical educators must prepare clinicians to critically appraise model outputs, interpret uncertainty, communicate transparently with patients, and engage in continuous monitoring within their institutions. The goal is not to replace clinical judgment but to strengthen it – ensuring that AI becomes a safe, reliable partner in decision-making across the spectrum of acute care, chronic disease management, and procedural practice.</p> |
| doi_str_mv | 10.5772/intechopen.1014649 |
| first_indexed | 2026-10-15T21:41:32Z |
| format | Book Chapter |
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<dc:title>From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery</dc:title>
<dc:creator>Khadeja Alrefaie</dc:creator>
<dc:creator>Basel Madlol</dc:creator>
<dc:creator>Oday Atallah</dc:creator>
<dc:subject>Health Sciences</dc:subject>
<dc:description><p>Artificial intelligence (AI) is now woven into the everyday clinical practice of internal medicine, neurology, neurosurgery, and dermatology, transforming how clinicians triage, diagnose, document, and operate. From the early rule-based systems of the 1970s to today&rsquo;s deep-learning platforms and multimodal foundation models, the evolution of medical AI reflects simultaneous advances in data availability, computational power, and regulatory oversight. Contemporary tools &ndash; automated stroke triage, rapid-response EEG, seizure-detection wearables, dermatologic image classifiers, and augmented-reality surgical navigation &ndash; have accelerated clinical workflows while raising new questions about validation, safety, and equity. This chapter traces that trajectory and examines the distinction between regulatory clearance, technical accuracy, and true clinical benefit. It outlines evidence standards across specialties, highlights lessons from successful and unsuccessful deployments, and provides a practical governance and education framework for integrating AI responsibly into training programs. As AI moves from proof-of-concept to routine care, medical educators must prepare clinicians to critically appraise model outputs, interpret uncertainty, communicate transparently with patients, and engage in continuous monitoring within their institutions. The goal is not to replace clinical judgment but to strengthen it &ndash; ensuring that AI becomes a safe, reliable partner in decision-making across the spectrum of acute care, chronic disease management, and procedural practice.</p></dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2026-05-06</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/1237227</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.1014649</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-80631-094-4</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/1004945 ; Medical Education in the Age of Digital Transformation</dc:source>
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| id | intech-1237227 |
| institution | IntechOpen |
| isbn | 978-1-80631-094-4 |
| language | English |
| last_indexed | 2026-10-15T21:41:32Z |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-12372272026-05-06 From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery Khadeja Alrefaie Basel Madlol Oday Atallah Health Sciences <p>Artificial intelligence (AI) is now woven into the everyday clinical practice of internal medicine, neurology, neurosurgery, and dermatology, transforming how clinicians triage, diagnose, document, and operate. From the early rule-based systems of the 1970s to today’s deep-learning platforms and multimodal foundation models, the evolution of medical AI reflects simultaneous advances in data availability, computational power, and regulatory oversight. Contemporary tools – automated stroke triage, rapid-response EEG, seizure-detection wearables, dermatologic image classifiers, and augmented-reality surgical navigation – have accelerated clinical workflows while raising new questions about validation, safety, and equity. This chapter traces that trajectory and examines the distinction between regulatory clearance, technical accuracy, and true clinical benefit. It outlines evidence standards across specialties, highlights lessons from successful and unsuccessful deployments, and provides a practical governance and education framework for integrating AI responsibly into training programs. As AI moves from proof-of-concept to routine care, medical educators must prepare clinicians to critically appraise model outputs, interpret uncertainty, communicate transparently with patients, and engage in continuous monitoring within their institutions. The goal is not to replace clinical judgment but to strengthen it – ensuring that AI becomes a safe, reliable partner in decision-making across the spectrum of acute care, chronic disease management, and procedural practice.</p> IntechOpen 2026-05-06 Chapter, Part Of Book https://www.intechopen.com/chapters/1237227 doi:10.5772/intechopen.1014649 en ISBN:978-1-80631-094-4 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/1004945 ; Medical Education in the Age of Digital Transformation |
| spellingShingle | Health Sciences Alrefaie, Khadeja Madlol, Basel Atallah, Oday From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery |
| title | From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery |
| title_full | From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery |
| title_fullStr | From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery |
| title_full_unstemmed | From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery |
| title_short | From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery |
| title_sort | from expert systems to ambient and augmented care ai in internal medicine dermatology neurology and neurosurgery |
| topic | Health Sciences |
| url | https://www.intechopen.com/chapters/1237227 |
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