Enterprise AI Analysis
AI and personalized medicine in healthcare: algorithmic normativity and practice configurations in danish healthcare education
This analysis explores how Personalized Medicine (PM) and Artificial Intelligence (AI) are reconfiguring healthcare practices, professional identities, and ethical norms in Danish healthcare education. We apply Shove's practice theory to examine the dynamic interplay of technologies, competencies, and meanings, conceptualizing the shift as 'algorithmic normativity' that actively shapes normative frameworks.
Executive Impact: Navigating Algorithmic Normativity
Our findings reveal that AI's integration into personalized medicine in healthcare is not just a technological adoption but a profound transformation of practice linkages. It demands a strategic approach to manage shifting professional roles, ethical dilemmas, and the need for interdisciplinary collaboration.
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Understanding Algorithmic Normativity in Healthcare
This concept highlights how algorithms move beyond mere support, actively shaping the norms, values, and ethical frameworks governing clinical decisions. It involves embedding new standards and redistributing accountability.
Shifting Norms
"Algorithmic normativity refers to the process through which algorithmic systems reconfigure professional norms and responsibilities by embedding new standards, redistributing accountability, and shaping epistemic dependencies within clinical practice." (p.3)
Reconfiguring Healthcare Practices via Shove's Theory
Drawing on Shove's practice theory, the study illustrates how the introduction of AI and PM acts as a catalyst for transforming healthcare work through the interaction of materials, competences, and meanings.
Enterprise Process Flow
New Materials (AI Tools & Data)
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Evolving Competences (Data Literacy)
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Reshaped Meanings (Ethical Values)
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Reconfigured Healthcare Practices
Ethical Tensions in AI-Driven Healthcare
The integration of AI and PM brings forth significant ethical dilemmas, challenging traditional frameworks around patient care, data use, and accountability.
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Redefining Professional Identity in the AI Era
AI challenges established professional jurisdictions, redefining roles and demanding new forms of expertise, leading to potential erosion of clinical judgment and a need for interdisciplinary collaboration.
The Physician as Algorithm: Blurring Roles
Context: Participants grapple with the tension between clinical judgment and algorithmic outputs, leading to questions like "Should I still use my clinical judgment, or should I ask the algorithm what the patient has?" (p.8) This reflects a profound shift where the "doctor is a kind of algorithm."
Challenge: The study highlights how algorithmic normativity reshapes professional identity, requiring clinicians to integrate machine logic into their embodied judgment, transforming medical art into a new form of practice. There's a concern about "unlearning clinical judgment" due to over-reliance on opaque AI outputs.
Resolution: This demands new competences in critical assessment of AI outputs, fostering interdisciplinary dialogue between clinicians and data scientists, and ethical reflection to maintain trust and responsibility in the evolving clinical landscape.
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Strategic Roadmap for AI Integration in Healthcare
Implementing AI and personalized medicine requires a phased approach that addresses materials, competences, and meanings to ensure sustainable transformation.
Phase 1: Pilot & Infrastructure Development
Integrate new AI tools and data infrastructures, establishing robust data ecosystems for precision medicine initiatives. Focus on secure and reliable data consolidation.
Phase 2: Competence Building & Training
Develop data literacy, AI interpretability skills, and foster interdisciplinary collaboration among healthcare professionals, data scientists, and ethicists.
Phase 3: Ethical Frameworks & Governance
Address concerns around algorithmic bias, data ownership, informed consent, and accountability through robust ethical guidelines and regulatory compliance.
Phase 4: Workflow Reconfiguration & Adaptation
Redefine clinical routines and professional roles to effectively leverage AI outputs while preserving human judgment and patient-centered care. Reconfigure accountability structures.
Phase 5: Continuous Evaluation & Iteration
Establish mechanisms for ongoing assessment of AI system performance, ethical implications, and professional adaptation to evolving technologies and normative shifts.
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