@article{2984745,
    title = "Remote AI Supported E-Multidisciplinary Oncology Conference in Breast Cancer as a Technology and Method to Optimize Outcomes in the Peripheries",
    author = "Mammas, C.S. and Mamma, A.S. and Papaxoinis, G. and Georgiou, I.",
    journal = "Studies in Health Technology and Informatics",
    year = "2022",
    volume = "289",
    pages = "309-312",
    publisher = "IOS Press BV",
    doi = "10.3233/SHTI210921",
    keywords = "Cytology;  Decision making;  Diagnosis;  Diseases;  Multimedia systems;  Pathology;  Risk analysis;  Risk assessment;  Surgery, Breast Cancer;  Breast carcinomas;  Breast surgery;  Classifieds;  Decisions makings;  E-multidisciplinary oncology conference;  Patient's suffering;  Tele-cytology;  Telemedicine systems;  Teleradiology, Oncology, artificial intelligence;  breast tumor;  female;  human;  reproducibility;  technology;  tumor recurrence, Artificial Intelligence;  Breast Neoplasms;  Female;  Humans;  Neoplasm Recurrence, Local;  Reproducibility of Results;  Technology",
    abstract = "Feasibility-reliability control of Telemedicine Systems (TS) integrated with Multimedia Systems (MS) and Artificial intelligence (AI) for remote e-Multidisciplinary Oncology Conference in Breast Cancer. Material and Methods: Forty (n1=40) patients suffering from breast surgical oncology malignant (n2=32) and non-malignant (n3=8) diseases classified to seven categories: Nipple Discharge, Dominant Breast Mass, Occult Breast Lesion, Early Breast Carcinoma, Advanced Breast Carcinoma, Recurrent Breast Carcinoma) and treated clinically with the standard diagnostic (Mammography, US, MRI, Cytology, Pathology, BRCA1/2 Mutation Predisposition and Breast Cancer Risk Analysis) surgical, auxiliary therapeutic methods. Then clinical decisions compared to those proposed remotely by the virtual AI supported e-Oncology Conference for each patient. Results: In four (n4=4) out of forty patients (TS, MS and AI) supported decision making and surgical treatment proposal including postoperative Radiotherapy proposal was not as clear as expected. Non-output answer for non-malignant breast pathologies (n3=8) was accurately indicated by (MS and AI). Mean accuracy of (TS, MS and AI) for: 1.Surgical Operative Planning including Rad=94.1%, 2.Chem=96.8%, 3.Horm=96.7% [In 95%, (Confidence interval: 85-99%)]. Conclusion: High feasibility-reliability of the virtual AI supported e-Multidisciplinary Oncology Conference for remote decision making and surgical planning and for optimum outcomes in Breast Cancer treatment makes it a clinical necessity especially for the periphery of Hellas. © 2022 The authors and IOS Press."
}