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Impact of artificial intelligence on pathologists' decisions: an experiment.
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Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight.
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Harnessing artificial intelligence for prostate cancer management.
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Organizational preparedness for the use of large language models in pathology informatics.
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Machine learning in renal pathology.
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Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
Med Image Anal. 2022 Jul;79:102470. doi: 10.1016/j.media.2022.102470. Epub 2022 May 4.
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Human-computer collaboration for skin cancer recognition.
Nat Med. 2020 Aug;26(8):1229-1234. doi: 10.1038/s41591-020-0942-0. Epub 2020 Jun 22.
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Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians.
J Med Internet Res. 2020 Jun 19;22(6):e15154. doi: 10.2196/15154.
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Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry.
Acad Radiol. 2021 Jun;28(6):834-840. doi: 10.1016/j.acra.2020.04.011. Epub 2020 May 13.
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.
Lancet Oncol. 2020 Feb;21(2):222-232. doi: 10.1016/S1470-2045(19)30738-7. Epub 2020 Jan 8.
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Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.
Nat Rev Clin Oncol. 2019 Nov;16(11):703-715. doi: 10.1038/s41571-019-0252-y. Epub 2019 Aug 9.
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Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.
NPJ Digit Med. 2019 Jun 7;2:48. doi: 10.1038/s41746-019-0112-2. eCollection 2019.
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Physician perspectives on integration of artificial intelligence into diagnostic pathology.
NPJ Digit Med. 2019 Apr 26;2:28. doi: 10.1038/s41746-019-0106-0. eCollection 2019.

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