Kunstig intelligens til klinisk billeddiagnostik

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Kunstig intelligens til klinisk billeddiagnostik. / Ladefoged, Claes Nøhr; Andersen, Flemming Littrup; Højgaard, Liselotte.

In: Ugeskrift for Laeger, Vol. 182, No. 13, V10190563, 2020.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Ladefoged, CN, Andersen, FL & Højgaard, L 2020, 'Kunstig intelligens til klinisk billeddiagnostik', Ugeskrift for Laeger, vol. 182, no. 13, V10190563. <https://ugeskriftet.dk/files/scientific_article_files/2020-03/ufl-10-19-0563-file008-digital_tv_.pdf>

APA

Ladefoged, C. N., Andersen, F. L., & Højgaard, L. (2020). Kunstig intelligens til klinisk billeddiagnostik. Ugeskrift for Laeger, 182(13), [V10190563]. https://ugeskriftet.dk/files/scientific_article_files/2020-03/ufl-10-19-0563-file008-digital_tv_.pdf

Vancouver

Ladefoged CN, Andersen FL, Højgaard L. Kunstig intelligens til klinisk billeddiagnostik. Ugeskrift for Laeger. 2020;182(13). V10190563.

Author

Ladefoged, Claes Nøhr ; Andersen, Flemming Littrup ; Højgaard, Liselotte. / Kunstig intelligens til klinisk billeddiagnostik. In: Ugeskrift for Laeger. 2020 ; Vol. 182, No. 13.

Bibtex

@article{181b2fa4f5d346709a56100cbbf17aa4,

title = "Kunstig intelligens til klinisk billeddiagnostik",

abstract = "Artificial intelligence (AI) is a computer-based system, which in diagnostic imaging can improve patient flow, optimise image processing, shorten scan time, reduce radiation dose and be used as decision aid in image interpretation. In this review, we argue that AI algorithms should be based on evidence with initial hypothesis, then a choice of algorithm and development with training on the initial data set; afterwards the algorithms should be tested on a new representative dataset, and finally it should be tested in a prospective study. If the AI is evidence-based and can solve a task better or cheaper than the usual methodology, it can be implemented.",

author = "Ladefoged, {Claes N{\o}hr} and Andersen, {Flemming Littrup} and Liselotte H{\o}jgaard",

year = "2020",

language = "Dansk",

volume = "182",

journal = "Ugeskrift for Laeger",

issn = "0041-5782",

publisher = "Almindelige Danske Laegeforening",

number = "13",

}

RIS

TY - JOUR

T1 - Kunstig intelligens til klinisk billeddiagnostik

AU - Ladefoged, Claes Nøhr

AU - Andersen, Flemming Littrup

AU - Højgaard, Liselotte

PY - 2020

Y1 - 2020

N2 - Artificial intelligence (AI) is a computer-based system, which in diagnostic imaging can improve patient flow, optimise image processing, shorten scan time, reduce radiation dose and be used as decision aid in image interpretation. In this review, we argue that AI algorithms should be based on evidence with initial hypothesis, then a choice of algorithm and development with training on the initial data set; afterwards the algorithms should be tested on a new representative dataset, and finally it should be tested in a prospective study. If the AI is evidence-based and can solve a task better or cheaper than the usual methodology, it can be implemented.

AB - Artificial intelligence (AI) is a computer-based system, which in diagnostic imaging can improve patient flow, optimise image processing, shorten scan time, reduce radiation dose and be used as decision aid in image interpretation. In this review, we argue that AI algorithms should be based on evidence with initial hypothesis, then a choice of algorithm and development with training on the initial data set; afterwards the algorithms should be tested on a new representative dataset, and finally it should be tested in a prospective study. If the AI is evidence-based and can solve a task better or cheaper than the usual methodology, it can be implemented.

M3 - Tidsskriftartikel

C2 - 32285782

VL - 182

JO - Ugeskrift for Laeger

JF - Ugeskrift for Laeger

SN - 0041-5782

IS - 13

M1 - V10190563

ER -

ID: 269793243