TY - JOUR
AU - J. Penhaskashi
AU - J. Danesh
AU - A. Naeim
AU - J. Golshirazi
AU - J. Hedvat
AU - F. Chiappelli
A1 - 
AB - The integration of Artificial Intelligence (AI) in to the field of medicine is offering a new-age of updated diagnostics, prediction and treatment across multiple fields, addressing systemic disease including viral infections and cancer. The fields of Oral Pathology, Dermatology, Psychiatry and Cardiology are shifting towards integrating these algorithms to improve health outcomes. AI trained on biomarkers (e.g. salivary cf DNA) has shown to uncover the genetic linkage to disease and symptom susceptibility. AI-enhanced imaging has increased sensitivity in cancer and lesion detection, as well as detecting functional abnormalities not clinically identified. The integration of AI across fields enables a systemic approach to understanding chronic inflammation, a central driver in conditions like cardiovascular disease, diabetes and neuropsychiatric disorders. We propose that through the use of imaging data with biomarkers like cytokines and genetic variants, AI models can better trace the effects of inflammation on immune and metabolic disruptions. This can be applied to the pandemic response, where AI can model the cascading effects of systemic dysfunctions, refine predictions of severe outcomes and guide targeted interventions to mitigate the multi-systemic impacts of pathogenic diseases.
AD - Division of West Valley Dental Implant Center, Encino, California 91316.; University of California, Los Angeles.; Independent Researcher, UCLA Health.; Center for the Health Sciences, UCLA, Los Angeles, California and Dental Group of Sherman Oaks, California 91403.
AN - 40322698
BT - Bioinformation
C5 - HIT & Telehealth
CP - 2
DO - 10.6026/973206300210105
DP - NLM
ET - 20250228
IS - 2
JF - Bioinformation
LA - eng
N2 - The integration of Artificial Intelligence (AI) in to the field of medicine is offering a new-age of updated diagnostics, prediction and treatment across multiple fields, addressing systemic disease including viral infections and cancer. The fields of Oral Pathology, Dermatology, Psychiatry and Cardiology are shifting towards integrating these algorithms to improve health outcomes. AI trained on biomarkers (e.g. salivary cf DNA) has shown to uncover the genetic linkage to disease and symptom susceptibility. AI-enhanced imaging has increased sensitivity in cancer and lesion detection, as well as detecting functional abnormalities not clinically identified. The integration of AI across fields enables a systemic approach to understanding chronic inflammation, a central driver in conditions like cardiovascular disease, diabetes and neuropsychiatric disorders. We propose that through the use of imaging data with biomarkers like cytokines and genetic variants, AI models can better trace the effects of inflammation on immune and metabolic disruptions. This can be applied to the pandemic response, where AI can model the cascading effects of systemic dysfunctions, refine predictions of severe outcomes and guide targeted interventions to mitigate the multi-systemic impacts of pathogenic diseases.
PY - 2025
SN - 0973-2063 (Print); 0973-2063
SP - 105
EP - 109+
ST - Artificial intelligence in systemic diagnostics: Applications in psychiatry, cardiology, dermatology and oral pathology
T1 - Artificial intelligence in systemic diagnostics: Applications in psychiatry, cardiology, dermatology and oral pathology
T2 - Bioinformation
TI - Artificial intelligence in systemic diagnostics: Applications in psychiatry, cardiology, dermatology and oral pathology
U1 - HIT & Telehealth
U3 - 10.6026/973206300210105
VL - 21
VO - 0973-2063 (Print); 0973-2063
Y1 - 2025
ER -