Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow Review
| Authors: | Cobanaj, M.; Corti, C.; Dee, E. C.; McCullum, L.; Boldrini, L.; Schlam, I.; Tolaney, S. M.; Celi, L. A.; Curigliano, G.; Criscitiello, C. |
| Review Title: | Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow |
| Abstract: | Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients’ survival and quality of life. © 2023 The Authors |
| Keywords: | overall survival; review; treatment planning; clinical practice; quality of life; observer variation; data base; patient care; artificial intelligence; data analysis; image registration; personalized medicine; artificial neural network; decision support system; decision support; bias; pattern recognition; feature extraction; low-dose computed tomography; natural language processing; equity; digitalization; human; precision medicine; feature selection; fairness; malignant neoplasm; obstetric delivery; observer bias; diversity, equity and inclusion; inclusivity |
| Journal Title: | European Journal of Cancer |
| Volume: | 198 |
| ISSN: | 0959-8049 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2024-02-01 |
| Start Page: | 113504 |
| Language: | English |
| DOI: | 10.1016/j.ejca.2023.113504 |
| PUBMED: | 38141549 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Source: Scopus |
