Artificial superintelligence alignment in healthcare.

Journal: Japanese journal of radiology
Published Date: Nov 14, 2025

Abstract

The emergence of Artificial Superintelligence (ASI) in healthcare presents unprecedented opportunities for revolutionizing diagnostics, treatment planning, and population health management, but also introduces critical risks if these systems are not properly aligned with human values and clinical objectives. This review examines the theoretical foundations of ASI and the alignment problem in healthcare contexts, exploring how misaligned Artificial Intelligence (AI) systems could optimize for wrong objectives or pursue harmful strategies leading to patient harm and systemic failures. Current challenges in AI alignment are illustrated through real-world examples from radiology and clinical decision-making, where algorithms have demonstrated concerning biases, generalizability failures, and optimization for inappropriate proxy measures. The paper analyzes key alignment challenges including objective complexity and technical pitfalls, bias and fairness issues in healthcare data, ethical integration concerns involving compassion and patient autonomy, and system-level policy challenges around regulation and liability. Technical alignment strategies are discussed including reinforcement learning from human feedback, interpretability requirements, formal verification methods, and adversarial testing approaches. Normative alignment solutions encompass ethical frameworks, professional standards, patient engagement protocols, and multi-level governance structures spanning institutional, national, and international coordination. The review emphasizes that successful ASI alignment in healthcare requires combining cutting-edge AI research with fundamental medical ethics, noting that while proper alignment could enable transformative health improvements and medical breakthroughs, misalignment risks undermining the core purpose of medicine. The stakes of this alignment challenge are characterized as among the highest in both technology and ethics, with implications extending from individual patient safety to public trust and potentially existential risks.

Authors

  • Daiju Ueda
    Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan. [email protected].
  • Shannon L Walston
    Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
  • Ryo Kurokawa
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Tsukasa Saida
    Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
  • Maya Honda
    From the Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan (M.I.); Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.I., M.K., M.H., Y.N.); A.I. System Research, Kyoto, Japan (R.M.); Kyoto University Faculty of Medicine, Kyoto, Japan (K.T., T.Y.); Department of Diagnostic Radiology, Kyoto City Hospital, Kyoto, Japan (A.M.); Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan (M.H.); e-Growth, Kyoto, Japan (K.I.); and Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan (M.T.).
  • Mami Iima
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University, Graduate School of Medicine, Kyoto, Japan.
  • Tadashi Watabe
    Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Masahiro Yanagawa
    Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
  • Kentaro Nishioka
    Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan.
  • Keitaro Sofue
    Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe City, Hyogo 650-0017, Japan.
  • Akihiko Sakata
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-ku Kyoto 606-8507, Japan.
  • Shunsuke Sugawara
    Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan.
  • Mariko Kawamura
    Department of Radiology, Nagoya University Graduate School of Medicine.
  • Rintaro Ito
    Department of Innovative Biomedical Visualization, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Japan.
  • Koji Takumi
    Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Seitaro Oda
    Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (T.N., N.Y., N.K., Y.N., H.U., M.K., S.O., T.H.).
  • Kenji Hirata
    Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
  • Satoru Ide
    Department of Radiology, University of Occupational and Environmental Health, School of Medicine.
  • Shinji Naganawa
    Department of Radiology, Nagoya University Graduate School of Medicine.

Keywords

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