NAVIGATING THE SOCIO-TECHNICAL SHIFT: A SYSTEMATIC REVIEW OF PATIENT TRUST, ANXIETY, AND INFORMED CONSENT IN AI-ENHANCED MAMMOGRAPHY (2022-2026)

Abstract

The integration of Artificial Intelligence (AI) into breast cancer screening represents a profound socio-technical shift rather than a mere technical upgrade. This systematic review synthesizes evidence from 23 unique studies published between 2022 and 2026 to explore how patients navigate the transition toward algorithmic diagnostics. The analysis focuses on three core pillars: the architecture of trust, the modulation of diagnostic anxiety, and the evolution of informed consent standards. The findings reveal that patient trust is strictly conditional, with a significant preference for "Second Reader" models over autonomous triage. This underscores the necessity of a "Human-in-the-Loop" framework where the radiologist remains the moral anchor of the diagnostic journey. Paradoxically, while technical literacy among patients remains low, AI is shown to significantly reduce the "anxiety gap" by facilitating same-day results and reducing false-positive recalls by up to 25%. Furthermore, the review identifies a "Safety-Net Paradox," where underserved populations view AI as an objective equalizer against potential human bias. However, current informed consent protocols often lag behind patient expectations; many women now consider AI involvement a "material fact" essential to their clinical autonomy. The review concludes that the successful implementation of AI in mammography requires a patient-centered framework that balances technological efficiency with the preservation of the human-radiologist connection. Ultimately, the success of AI will be measured not just by its sensitivity, but by its ability to protect the psychological integrity of the patient.

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