Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study.

Journal: European urology oncology
PMID: 38789385

Abstract

BACKGROUND AND OBJECTIVE: Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our aim was to evaluate the effectiveness and cost-effectiveness of AI-assisted pathology for PCa diagnosis in Sweden.

Authors

  • Xiaoyang Du
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: xiaoyang.du@ki.se.
  • Shuang Hao
    School of Science, Anhui Agricultural University, Hefei, 230036, China.
  • Henrik Olsson
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Kimmo Kartasalo
    BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
  • Nita Mulliqi
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Balram Rai
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Dominik Menges
    Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
  • Emelie Heintz
    Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden; Centre for Health Economics, Informatics and Health Services Research, Stockholm Health Care Services, Stockholm, Sweden.
  • Lars Egevad
    Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
  • Martin Eklund
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: martin.eklund@ki.se.
  • Mark Clements
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

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