ABSTRACT
Social and health care equity and justice are seldom achieved at local, regional, national or international levels despite the good faith efforts of national and global health strategies. There remain inequities of opportunity and access driven by intrinsic and extrinsic factors throughout a patient’s healthcare experiences, including in radiology and nuclear medicine. Artificial intelligence (AI) has the potential to either widen the health inequity divide or substantially reduce it. While there are a number of challenges to overcome in the AI pipeline, the potential to enhance patient-centred care by connecting resources and expertise through an AI platform needs to be considered.
AI in nuclear medicine and radiology could eventually emerge as a powerful tool in social and health equity but is already producing improvements in workflow and person-centred care. AI has the potential to reinforce institutional and historical biases but careful curation of data may allow AI to be used to not only identify bias, but also to eliminate it; enhancing patient-centred care and precision medicine. AI, especially through deep learning approaches, affords the chance to interrogate individual patient images through an abstract deep learning lens that could identify unique radiomic features of the individual patient not otherwise detectable. In doing so, AI augmented image interpretation can accommodate unique features of individual patients and enhance both person-centred care and precision medicine.
