Patient Centred AI

Shamie Kumar^*

• Qure.ai, London, United Kingdom

These findings demonstrate that AI not only enhances diagnostic accuracy and reader confidence but also streamlines interpretation workflows, reducing missed findings and expediting time to diagnosis. By embedding AI into standard practice, evidence demonstrates clinicians are better equipped to identify disease such as lung cancer earlier, enabling timelier intervention and improved patient outcomes.We now look at changing the care pathway from reactive to proactive and what makes this truly patient-centered is not just the use of cutting-edge AI, but how it enhances the patient's pathway in a practical manner:• Reduced waiting times: AI processes chest X-rays immediately following acquisition, detecting and triaging findings suspicious of lung cancer for faster reporting. In the RADICAL study (Duncan, 2025), researchers evaluated the integration of qXR into NHS workflows and will review how AI-enhanced triage has reduced average reporting times and expedited CT follow-ups for suspected lung cancer cases, especially in resource stretched hospitals.• Earlier detection: Patients who may otherwise have gone undiagnosed for months or years are identified early, leading to better outcomes (World Health Organization, 2021; Kaviani et al, 2022). AI-assisted CXR has demonstrated the potential to identify lung cancer at an earlier stage, including in patients who may have otherwise remained undiagnosed for extended periods, thereby improving prognosis and enabling timely intervention (Hutchison, 2024). This is particularly impactful in healthcare systems serving large, diverse populations, where diagnostic delays are more common due to staffing and capacity constraints.• Minimising patient anxiety: Faster, shorter and clearer diagnostic pathways help reduce the mental burden often associated with uncertainty and delays. The promise of AI is not without risk. Poorly designed algorithms can perpetuate health inequities, while over-reliance on systems may erode clinical accountability. Several principles of patient-centered AI are emerging:• Transparency and trust: Patients need to know when AI is involved in their care, and how decisions are made. Clinicians remain the ultimate decisionmakers, ensuring AI serves as an augmentation, not a replacement. (O'Sullivan et al, 2022; Gerke, Minssen and Cohen, 2020; Leslie, 2021).• Equity: A patient-centered approach ensures that AI benefits are distributed equally across demographics, not favoring certain groups. Broader deployment aims to detect cancers irrespective of socioeconomic background (Gerke, Minssen and Cohen, 2020). These elements ensure AI not only improves outcomes but also fosters trust, compassion, and continuity in care. Patient-centered AI creates a system where technology amplifies rather than replaces human care. Earlier diagnoses, faster pathways, and more personalised care offer patients not just better outcomes, but better lives. When AI is deployed thoughtfully, ethically, and with the patient at the centre of every decision, we get closer to a model of care where innovation serves people first.