Review paper (Pregledni rad)
Mihovil Delija1, Frane Mihanović1
1University of Split, University Department of Health Studies, Split, Croatia
Corresponding author: Mihovil Delija, e mail:
DOI: https://doi.org/10.55378/rv.48.2.2
Abstract
Artificial intelligence (AI) is bringing changes to radiology and radiologic technology, enabling the development of programs and algorithms that facilitate diagnosis and decisions. It is essential to understand how AI can improve patient outcomes, increase the efficiency of investigations and reduce costs. Machine and deep learning have proven to be extremely useful in the detection and characterization of lesions, improving routine imaging techniques and facilitating the work of radiologists by reducing workload and improving the quality of reporting. The practical application of artificial intelligence in radiology and radiologic technology has been slowed down by the lack of integrated solutions and well-structured data archives, as well as challenges such as non-transparency of decision-making systems and large amounts of quality data needed to train artificial intelligence models. There are concerns about the potential impact of AI on the work of radiologists and radiologic technologists, which may hinder the development and implementation of this technology. Textual data derived from image reports can provide valuable healthcare insights. Natural language processing (NLP), a subset of artificial intelligence, offers promising solutions for handling unstructured text in these reports, opening a new era in extracting information from medical images and related reports. Due to the challenges in training experts for the application of AI in healthcare, a multidisciplinary approach will have to be used and investments in collaboration and education will have to be made. There are outstanding issues of liability and regulation regarding data storage and privacy, particularly in the case of cloud storage. The concern of the workforce and their lack of education about artificial intelligence represents an obstacle to its adoption, but also offers an opportunity for the technological advancement of the profession.
Keywords: AI, radiologic technology, radiology, SWOT
Abbreviations and acronyms: AI (Artificial intelligence), CAD (Computer-Aided Diagnosis), CDS (Clinical Decision Support), CNN (Convolutional Neural Networks), CT (Computed Tomography), DL (Deep learning), GAN (Generative Adversarial Network), FDA (Food and Drug Administration), ML (Machine Learning), NLP (Natural Language Processing)
SWOT analiza primjene umjetne inteligencije u radiološkoj tehnologiji i radiologiji
Sažetak
Umjetna inteligencija (AI) unosi promjene u radiologiju i radiološku tehnologiju, omogućavajući razvoj programa i algoritama koji olakšavaju dijagnozu i odluke. Bitno je razumjeti na koji način AI može poboljšati ishode pacijenata, povećati učinkovitost pretraga i smanjiti troškove. Strojno i duboko učenje pokazali su se kao iznimno korisni u otkrivanju i karakterizaciji lezija, poboljšavajući rutinske tehnike snimanja i olakšavajući rad radiologa smanjenjem opterećenja i poboljšanjem kvalitete izvještavanja. Praktična primjena umjetne inteligencije u radiologiji i radiološkoj tehnologiji usporena je nedostatkom integriranih rješenja i dobro strukturiranih arhiva podataka, a također i izazovima kao što su netransparentnost sustava kod donošenja odluka i velika količine kvalitetnih podataka potrebnih za obuku modela umjetne inteligencije. Postoji zabrinutost zbog potencijalnog utjecaja AI na posao radiologa i radioloških tehnologa što može kočiti razvoj i primjenu ove tehnologije. Tekstualni podaci izvedeni iz slikovnih izvješća mogu pružiti vrijedne uvide u zdravstvu. Obrada prirodnog jezika (NLP), podskup umjetne inteligencije, nudi obećavajuća rješenja za rukovanje nestrukturiranim tekstom u ovim izvješćima, što otvara novu eru u izdvajanju informacija iz medicinskih slika i pripadajućih izvješća. Zbog izazova u obuci stručnjaka za primjenu AI-a u zdravstvu morat će se koristiti multidisciplinarni pristup i ulagati u suradnju i obrazovanje. Postoje neriješena pitanja o odgovornosti i regulaciji u vezi s pohranjivanjem podataka i privatnosti, osobito u slučaju pohrane podataka na sistemu „oblaka“. Zabrinutost i neinformiranost radne snage o umjetnoj inteligenciji predstavlja prepreku za njeno usvajanje, ali i nudi priliku za tehnološko napredovanje struke.
Ključne riječi: AI, radiologija, radiološka tehnologija, SWOT
