Title
Artificial intelligence in systemic diagnostics: Applications in psychiatry, cardiology, dermatology and oral pathology
Authors
Jaden Penhaskashi1, Jonah Danesh2, Arya Naeim3, Josh Golshirazi2, Justin Hedvat2 & Francesco Chiappelli*, 4
Affiliation
1Division of West Valley Dental Implant Center, Encino, CA 91316; 2University of California, Los Angeles; 3Independent Researcher, UCLA Health; 4Center for the Health Sciences, UCLA, Los Angeles, CA and Dental Group of Sherman Oaks, CA 91403; *Corresponding author
Jaden Penhaskashi - E - mail: jadennpen@gmail.com
Jonah Danesh - E - mail: jonahdanesh@gmail.com
Arya Naeim - E - mail: aryanaeim@ucla.edu
Josh Golshirazi - E - mail: joshgol@ucla.edu
Justin Hedvat - E - mail: hedvatjustin@ucla.edu
Francesco Chiappelli - E - mail: Chiappelli.research@gmail.com
Article Type
Editorial
Date
Received February 1, 2025; Revised February 28, 2025; Accepted February 28, 2025, Published February 28, 2025
Abstract
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 cfDNA) 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.
Keywords
Artificial intelligence, machine learning, deep learning, convolutional neural networks, neural networks, single nucleotide polymorphisms, genetic biomarkers, systemic inflammation, diagnostic imaging, functional MRI, cardiac magnetic resonance, fractional amplitude of low-frequency fluctuations, cardiovascular disease, digital biomarkers, psychiatry, cardiology, dermatology, oral pathology
Citation
Penhaskashi et al. Bioinformation 21(2): 105-109 (2025)
Edited by
Francesco Chiappelli
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
