Responsible AI practice and AI education are central to AI implementation : a rapid review for all medical imaging professionals in Europe

Walsh, Gemma (avtor), Stogiannos, Nikolaos (avtor), Van de Venter, Riaan (avtor), Rainey, Claire (avtor), Tam, Winnie (avtor), McFadden, Sonyia (avtor), McNulty, Jonathan P. (avtor), Mekiš, Nejc (avtor), Lewis, Sarah (avtor), O'Regan, Tracy (avtor)

Objectives: It is essential to study the dosimetric performance and reliability of personal dosimeters. This study examines and compares the responses of two commercial thermoluminescence dosimeters (TLDs), the TLD-100 and the MTS-N. Methods: We compared the two TLDs to various parameters such as energy dependence, linearity, homogeneity, reproducibility, light sensitivity (zero point), angular dependence, and temperature effects using the IEC 61066 standard. Results: The results acquired showed that both TLD materials show linear behavior as indicated by the quality of the fit. In addition, the angular dependence results for both detectors show that all dose responses are within the range of acceptable values. However, the TLD-100 outperformed the MTS-N in terms of light sensitivity reproducibility for all detectors together, while the MTS-N outperforms the TLD-100 for each detector independently and that showed TLD-100 has more stability than MTS-N. The MTS-N shows better batch homogeneity (10.84%) than TLD-100 (13.65%). The effect of temperature in signal loss was clearer at higher temperature 65°C and it was however below ±30%. Conclusions: The overall results for dosimetric properties determined in terms of dose equivalents for all combinations of detectors are satisfactory. The MTS-N cards have better results in the energy dependence, angular dependency, batch homogeneity and less signal fading, whereas the TLD-100 cards are less sensitive to light and more reproducible. Advances in knowledge: Although previous studies showed several types of comparisons between TLDs, they have used limited parameters and different data analysis. This study has dealt with more comprehensive characterization methods and examinations combining TLD-100 and MTS-N cards.

• Jezik: Angleški jezik
• Ključne besede: artificial intelligence, healthcare, radiology, radiography, big data, ethical practices
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.02 - Pregledni znanstveni članek
• Organizacija: ZF - Zdravstvena fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Datum objave: 30.06.2023
• Leto izida: 2023
• Št. strani: 9 str.
• Številčenje: Vol. 5, iss. 1, art. 20230033
• PID: 20.500.12556/RUL-150069
• UDK: 616-07:004.8
• ISSN pri članku: 2513-9878
• DOI: 10.1259/bjro.20230033
• COBISS.SI-ID: 157921795
• Datum objave v RUL: 13.09.2023

Gradivo je del revije

• Naslov: BJR|open
• Založnik: British Institute of Radiology
• ISSN: 2513-9878
• COBISS.SI-ID: 18870019

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 30.06.2023

Sekundarni jezik

• Jezik: Ni določen
• Ključne besede: umetna inteligenca, zdravstvo, radiologija, radiografija, veliki podatki, etične prakse