⚡ Quick Summary
This article discusses the transformative potential of human-artificial intelligence (AI) symbiotic reporting in theranostic cancer care, particularly for prostate cancer and neuroendocrine tumors. By integrating AI with clinical expertise, this approach aims to deliver personalized treatment and improve patient outcomes through enhanced diagnostic reporting.
🔍 Key Details
- 📊 Focus: Theranostic treatment using 177Lu radioligands
- 🧩 Key Technology: AI algorithms for data analysis
- ⚙️ Methodology: Integration of quantitative data with clinical expertise
- 🏆 Goals: Personalized treatment and improved patient safety
🔑 Key Takeaways
- 🤖 AI integration can revolutionize the reporting of diagnostic nuclear images.
- 💡 Personalized precision medicine is achievable through AI-enhanced reporting.
- 📈 Comprehensive reports will include quantitative analysis of imaging data.
- 🏥 Accurate dose prescription can minimize toxicity while maximizing treatment efficacy.
- 🔍 AI analysis can predict clinical outcomes based on tumor biology and patient history.
- 🌟 Enhanced accountability for nuclear physicians through AI-assisted reporting.
- 💬 Empathy and trust between physicians and patients can be fostered through this approach.
📚 Background
The current practice of nuclear oncology often employs a one-size-fits-all approach, particularly in the administration of 177Lu radioligands for cancer treatment. This method lacks the personalization that is crucial for effective cancer care. The integration of AI into this field presents an opportunity to enhance diagnostic accuracy and treatment personalization, moving towards a more tailored approach in theranostic cancer care.
🗒️ Study
The study emphasizes the need for a symbiotic relationship between AI and healthcare professionals. By leveraging AI’s quantitative data collection and analytic capabilities, physicians can provide a more comprehensive nuclear medicine report. This report would not only analyze diagnostic imaging but also calculate the radiation absorbed dose to both tumors and critical normal organs, ensuring a more effective treatment plan.
📈 Results
The proposed AI-enhanced reporting system aims to facilitate accurate dose prescriptions, ensuring that patients receive a tumoricidal radiation absorbed dose while minimizing potential toxicity. Additionally, post-therapy imaging will validate the actual dose delivered, allowing for ongoing adjustments in treatment based on individual patient responses.
🌍 Impact and Implications
The implications of this study are profound. By adopting a human-AI collaborative approach, the field of nuclear oncology can transition towards a more personalized and effective treatment paradigm. This not only enhances the quality of care but also empowers physicians to take greater responsibility for patient outcomes, fostering a deeper trust and connection with patients.
🔮 Conclusion
The integration of AI in theranostic cancer care represents a significant leap towards personalized medicine. By combining the strengths of AI with the empathy and expertise of healthcare professionals, we can improve diagnostic accuracy and treatment efficacy. The future of cancer care looks promising, and continued research in this area is essential for realizing its full potential.
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Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.
Abstract
Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with 177Lu radioligands for prostate cancer and neuroendocrine tumors is routinely given as the same arbitrary fixed administered activity to every patient. Nuclear oncology, as currently practiced with 177Lu-prostate-specific membrane antigen and 177Lu 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.
Author: [‘Turner JH’]
Journal: Cancer Biother Radiopharm
Citation: Turner JH. Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care. Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care. 2024; (unknown volume):(unknown pages). doi: 10.1089/cbr.2024.0216