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Three Problems with Big Data and Artificial Intelligence in Medicine.

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    Summary
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    Big data and artificial intelligence (AI) in healthcare offer promise but face significant philosophical challenges. Examining these issues is crucial for responsible AI integration in clinical practice.

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    Area of Science:

    • Healthcare technology
    • Medical ethics
    • Philosophy of science

    Background:

    • Big data and artificial intelligence (AI) are increasingly prominent in healthcare, promising advancements in diagnosis, prognosis, and treatment.
    • However, the underlying philosophical assumptions of this movement in medicine are often overlooked.
    • This gap necessitates a critical examination of the foundational principles guiding AI in healthcare.

    Purpose of the Study:

    • To identify and outline key philosophical challenges posed by big data and AI in healthcare.
    • To foster a critical dialogue regarding the implications of these technologies in clinical settings.
    • To encourage a more robust evaluation of AI and big data tools before widespread adoption.

    Main Methods:

    • Philosophical analysis of the theoretical underpinnings of big data and AI in medicine.
    • Identification of three core philosophical problems: epistemological-ontological, epistemological-logical, and phenomenological.
    • Literature review and conceptual argumentation to explore the limitations and challenges.

    Main Results:

    • Identified the epistemological-ontological problem related to theory-ladenness in big data and measurement.
    • Highlighted the epistemological-logical problem concerning algorithmic limitations, reliability, and interpretability.
    • Addressed the phenomenological problem of reducing human experience to quantitative data.

    Conclusions:

    • Philosophical challenges must be addressed for the responsible integration of big data and AI in healthcare.
    • These challenges impact the reliability, interpretability, and ethical application of AI tools.
    • Further critical dialogue is needed to develop healthcare approaches that effectively serve both clinicians and patients.