Human–Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care

Abstract

Lu peptide receptor radionuclide therapy, cannot, therefore, be characterized as personalized precision medicine. The evolution of artificial intelligence (AI) could change this "one-size-fits-all" approach to theranostics, through development of a symbiotic relationship with physicians. Combining quantitative data collection, collation, and analytic computing power of AI algorithms with the clinical expertise, empathy, and personal care of patients by their physician envisions a new paradigm in theranostic reporting for molecular imaging and radioligand treatment of cancer. Human-AI interaction will facilitate the compilation of a comprehensive, integrated nuclear medicine report. This holistic report would incorporate radiomics to quantitatively analyze diagnostic digital imaging and prospectively calculate the radiation absorbed dose to tumor and critical normal organs. The therapy activity could then be accurately prescribed to deliver a preordained, effective, tumoricidal radiation absorbed dose to tumor, while minimizing toxicity in the particular patient. Post-therapy quantitative imaging would then validate the actual dose delivered and sequential pre- and post-treatment dosimetry each cycle would allow individual dose prescription and monitoring over the entire course of theranostic treatment. Furthermore, the nuclear medicine report would use AI analysis to predict likely clinical outcome, predicated upon AI definition of tumor molecular biology, pathology, and genomics, correlated with clinical history and laboratory data. Such synergistic comprehensive reporting will enable self-assurance of the nuclear physician who will necessarily be deemed personally responsible and accountable for the theranostic clinical outcome. Paradoxically, AI may thus be expected to enhance the practice of phronesis by the nuclear physician and foster a truly empathic trusting relationship with the cancer patient.

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