AI in medical diagnosis: AI prediction & human judgment

被引:18
|
作者
Gondocs, Dora [1 ]
Dorfler, Viktor [2 ]
机构
[1] Szecheny Istvan Univ, Gyor, Hungary
[2] Univ Glasgow, Strathclyde Business Sch, Glasgow, Scotland
关键词
Medical diagnosis; Melanoma; Human -computer interaction; Augmented intelligence; Explainability; Responsible AI; ARTIFICIAL-INTELLIGENCE; HEALTH-CARE; SKIN-CANCER; SYSTEM; CLASSIFICATION; REPRESENTATION; INTUITION; KNOWLEDGE; NETWORKS; DESIGN;
D O I
10.1016/j.artmed.2024.102769
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, 'explainability', and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
    Calisto, Francisco Maria
    Santiago, Carlos
    Nunes, Nuno
    Nascimento, Jacinto C.
    ARTIFICIAL INTELLIGENCE IN MEDICINE, 2022, 127
  • [2] Domesticating AI in medical diagnosis
    Williams, Robin
    Anderson, Stuart
    Cresswell, Kathrin
    Kannelonning, Mari Serine
    Mozaffar, Hajar
    Yang, Xiao
    TECHNOLOGY IN SOCIETY, 2024, 76
  • [3] The role of AI technology in prediction, diagnosis and treatment of colorectal cancer
    Yu, Chaoran
    Helwig, Ernest Johann
    ARTIFICIAL INTELLIGENCE REVIEW, 2022, 55 (01) : 323 - 343
  • [4] Human-AI agency in the age of generative AI
    Krakowski, Sebastian
    INFORMATION AND ORGANIZATION, 2025, 35 (01)
  • [5] Medical AI and AI for Medical Sciences
    Sakurada, Kazuhiro
    Ishikawa, Tetsuo
    Oba, Junna
    Kuno, Masahiro
    Okano, Yuji
    Sakamaki, Tomomi
    Tamura, Tomohiro
    JMA JOURNAL, 2025, 8 (01): : 26 - 37
  • [6] Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review
    Subramanian, Harishankar V.
    Canfield, Casey
    Shank, Daniel B.
    ARTIFICIAL INTELLIGENCE IN MEDICINE, 2024, 149
  • [7] Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts
    Formosa, Paul
    Rogers, Wendy
    Griep, Yannick
    Bankins, Sarah
    Richards, Deborah
    COMPUTERS IN HUMAN BEHAVIOR, 2022, 133
  • [8] Medical AI and AI for Medical Sciences: An Editorial
    Kawakami, Eiryo
    JMA JOURNAL, 2025, 8 (01): : 38 - 39
  • [9] To Engage or Not to Engage with Al for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis
    Lebovitz, Sarah
    Lifshitz-Assaf, Hila
    Levina, Natalia
    ORGANIZATION SCIENCE, 2022, 33 (01) : 126 - 148
  • [10] Editorial: AI approach to the psychiatric diagnosis and prediction
    Gao, Wenjing
    Lu, Long
    Yin, Xuntao
    FRONTIERS IN PSYCHIATRY, 2024, 15