ANALYZING DISCREPANCIES BETWEEN AI-GENERATED AND ONCOLOGIST-FINALIZED TREATMENT RECOMMENDATIONS IN VIRTUAL CANCER CARE: IMPACT ON CLINICAL WORKFLOW AND PATIENT OUTCOMES

Abstract

The application of AI to virtual cancer care has made a tremendous impact on the clinical decision-making process, as it has aided the process of diagnosis, treatment planning, and therapeutic advice, but the gap between the AI-based treatment advice and the finalized decision of the oncologist has become a crucial issue. Such discrepancies can influence clinical practice, drug safety, and patient outcomes especially in digitally mediated oncology environments. The objective of this narrative review is to examine the nature and degree of difference between AI-generated and oncologist-finalized treatment advice during virtual cancer care and to investigate their effect on clinical workflow, oncology pharmacotherapy, medication safety, and patient outcomes. The literature search involved PubMed, Scopus, and Google Scholar, including those that were published as early as 2018 to 2026 and assessed AI-aided oncology decision-making, clinician-AI discordance, workflow implications, and patient-related outcomes. The examined literature indicates that there is a general range of 60-80 percent AI-clinician concordance, but the discrepancies are caused by both quantitative (regimen selection and line of therapy) and qualitative (patient comorbidities, preferences, and contextual clinical judgment) factors. These inconsistencies frequently require extra verification procedures, augment cognition, and expand the multidisciplinary decision-making. Notably, oncology pharmacists can have the key role of reducing the risk of AI-based recommendations by validating chemotherapy regimens, making sure of adequate dose, potential drug-drug interactions, and medication safety. To sum up, although AI has a potential to improve efficiency and standardization in virtual oncology care, the ongoing discrepancies highlight the significance of human-in-the-loop models, explainable AI systems, and active participation of oncology pharmacists to streamline the workflow, protect the use of medications, and promote patient-centered outcomes.