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Title:Optimizacija delovnih tokov v radiologiji z umetno inteligenco: pregled trenutnih dokazov
Authors:ID Konovalova, Ineia (Author)
ID Štiglic, Gregor (Mentor) More about this mentor... New window
ID Gosak, Lucija (Comentor)
Files:.pdf MAG_Konovalova_Ineia_2026.pdf (6,04 MB)
MD5: 1DE464362C6196DCF1EA6C36EE8364B4
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FZV - Faculty of Health Sciences
Abstract:Umetna inteligenca hitro revolucionira radiologijo, saj izboljšuje natančnost diagnoz, poenostavlja delovne tokove in zagotavlja podporo pri odločanju. Vendar pa je vključevanje umetne inteligence v vsakdanjo klinično prakso še vedno neenakomerno, saj ga ovirajo tehnične, etične, organizacijske in družbene težave. Ta raziskava predstavlja sistematičen pregled svetovne literature, pri čemer uporablja opisne in kompilacijske metode pregleda literature. Ugotovitve kažejo, da lahko umetna inteligenca izboljša stopnjo odkrivanja raka, pospeši nujne diagnoze in zagotovi upravljanje prek strukturiranih okvirov izvajanja. Hibridni modeli, ki združujejo umetno inteligenco s strokovnim znanjem radiologov, so zelo priljubljeni med pacienti in javnostjo, vendar ostajajo pomisleki glede preglednosti, odgovornosti in zaupanja. Klinični zdravniki poročajo o večji sprejemljivosti, kadar so orodja intuitivna in minimalno moteča, medtem ko so ovire nestabilno delovanje, nestrukturirano izvajanje in družbeno-organizacijski dejavniki. Kljub temu ostajajo kritične vrzeli glede dolgoročnih izidov za paciente, gospodarskega vpliva in integracije v okoljih z omejenimi viri. Umetna inteligenca ima transformativni potencial v radiologiji. Njena uspešna integracija bo odvisna od sodelovanja med različnimi zainteresiranimi stranmi, trdnosti upravljanja in pristopov usmerjenih v pacienta, podprtih z obsežnimi, prospektivnimi raziskavami.
Keywords:radiologija, umetna inteligenca, optimizacija, implementacija, potek dela
Place of publishing:Maribor
Publisher:[I. Konovalova]
Year of publishing:2026
PID:20.500.12556/DKUM-96449 New window
UDC:616-073.7+004.8(043.2)
COBISS.SI-ID:269725699 New window
Publication date in DKUM:12.03.2026
Views:134
Downloads:22
Metadata:XML DC-XML DC-RDF
Categories:FZV
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:09.01.2026

Secondary language

Language:English
Title:Optimizing radiology workflows with artificial intelligence: a review of current evidence
Abstract:Artificial intelligence is quickly revolutionizing radiology by enhancing diagnostic accuracy, streamlining workflows, and providing decision support. However, the integration of AI into everyday clinical practice remains uneven, hindered by technical, ethical, organizational, and social challenges. This study offers a systematic review of global literature, employing both descriptive and compilation methods. The findings indicate that artificial intelligence can improve cancer detection rates, accelerate urgent diagnoses, and provide governance through structured implementation frameworks. Hybrid models, which combine Artificial intelligence with radiologist expertise, are strongly preferred by patients and the public; however, concerns persist regarding transparency, accountability, and trust. Clinicians report greater acceptance when tools are intuitive and minimally disruptive, while barriers include unstable performance, unstructured implementation, and socio-organizational factors. Nonetheless, critical gaps remain regarding long-term patient outcomes, economic impact, and integration in low-resource contexts. Artificial intelligence has transformative potential in radiology. Its successful integration will depend on cross-stakeholder collaboration, robust governance, and patient-centered approaches, supported by large-scale, prospective research.
Keywords:radiology, artificial intelligence, optimization, implementation, workflow


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