BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

313 related articles for article (PubMed ID: 35429129)

  • 1. Australian perspectives on artificial intelligence in medical imaging.
    Currie G; Nelson T; Hewis J; Chandler A; Spuur K; Nabasenja C; Thomas C; Wheat J
    J Med Radiat Sci; 2022 Sep; 69(3):282-292. PubMed ID: 35429129
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Australian perspectives on artificial intelligence in veterinary practice.
    Currie G; Hespel AM; Carstens A
    Vet Radiol Ultrasound; 2023 May; 64(3):473-483. PubMed ID: 37022301
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Intelligent imaging: Applications of machine learning and deep learning in radiology.
    Currie G; Rohren E
    Vet Radiol Ultrasound; 2022 Dec; 63 Suppl 1():880-888. PubMed ID: 36514225
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers' perspectives.
    Aldhafeeri FM
    BMC Med Ethics; 2024 May; 25(1):52. PubMed ID: 38734602
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Intelligent Imaging in Nuclear Medicine: the Principles of Artificial Intelligence, Machine Learning and Deep Learning.
    Currie G; Rohren E
    Semin Nucl Med; 2021 Mar; 51(2):102-111. PubMed ID: 33509366
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers.
    Botwe BO; Akudjedu TN; Antwi WK; Rockson P; Mkoloma SS; Balogun EO; Elshami W; Bwambale J; Barare C; Mdletshe S; Yao B; Arkoh S
    Radiography (Lond); 2021 Aug; 27(3):861-866. PubMed ID: 33622574
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Nordic radiographers' and students' perspectives on artificial intelligence - A cross-sectional online survey.
    Pedersen MRV; Kusk MW; Lysdahlgaard S; Mork-Knudsen H; Malamateniou C; Jensen J
    Radiography (Lond); 2024 May; 30(3):776-783. PubMed ID: 38461583
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radiographers' knowledge, attitudes and expectations of artificial intelligence in medical imaging.
    Coakley S; Young R; Moore N; England A; O'Mahony A; O'Connor OJ; Maher M; McEntee MF
    Radiography (Lond); 2022 Nov; 28(4):943-948. PubMed ID: 35839662
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Radiation therapist perceptions on how artificial intelligence may affect their role and practice.
    O'Shaughnessey J; Collins ML
    J Med Radiat Sci; 2023 Apr; 70 Suppl 2(Suppl 2):6-14. PubMed ID: 36479610
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study.
    Botwe BO; Antwi WK; Arkoh S; Akudjedu TN
    J Med Radiat Sci; 2021 Sep; 68(3):260-268. PubMed ID: 33586361
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey.
    Akudjedu TN; Torre S; Khine R; Katsifarakis D; Newman D; Malamateniou C
    J Med Imaging Radiat Sci; 2023 Mar; 54(1):104-116. PubMed ID: 36535859
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine.
    Currie G; Hawk KE
    Semin Nucl Med; 2021 Mar; 51(2):120-125. PubMed ID: 33509368
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Empowering PET: harnessing deep learning for improved clinical insight.
    Artesani A; Bruno A; Gelardi F; Chiti A
    Eur Radiol Exp; 2024 Feb; 8(1):17. PubMed ID: 38321340
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives.
    Seifert R; Weber M; Kocakavuk E; Rischpler C; Kersting D
    Semin Nucl Med; 2021 Mar; 51(2):170-177. PubMed ID: 33509373
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Artificial Intelligence in Nuclear Medicine.
    Nensa F; Demircioglu A; Rischpler C
    J Nucl Med; 2019 Sep; 60(Suppl 2):29S-37S. PubMed ID: 31481587
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice.
    Abuzaid MM; Tekin HO; Reza M; Elhag IR; Elshami W
    Radiography (Lond); 2021 Oct; 27 Suppl 1():S83-S87. PubMed ID: 34364784
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Application of artificial intelligence in brain molecular imaging.
    Minoshima S; Cross D
    Ann Nucl Med; 2022 Feb; 36(2):103-110. PubMed ID: 35028878
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Radiography students' perceptions of artificial intelligence in medical imaging.
    Arruzza E
    J Med Imaging Radiat Sci; 2024 Jun; 55(2):258-263. PubMed ID: 38403517
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Artificial intelligence: The opinions of radiographers and radiation therapists in Ireland.
    Ryan ML; O'Donovan T; McNulty JP
    Radiography (Lond); 2021 Oct; 27 Suppl 1():S74-S82. PubMed ID: 34454835
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The transformational potential of molecular radiomics.
    Currie G; Hawk KE; Rohren E
    J Med Radiat Sci; 2023 Apr; 70 Suppl 2(Suppl 2):77-88. PubMed ID: 36238997
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.