Dr. Serhiy Hovornyan

Conference 2024 Live Talk

Talk Title

Concept of artificial intelligence oriented public health model in cancer care

Authors and Affiliations

Dr. Serhiy Hovornyan 1, Prof. Oleksandr Ivashchuk 1
1. Department of Oncology and Radiology, Bukovinian State Medical University, Chernivtsi, Ukraine

Abstract

Background

In the last few years, the amount of required information for oncological specialists critically increased. Due to the big amount of information and complexity of multifactorial cancer screening, diagnostic and treatment, it is very hard for the oncologist to stay updated with the latest data and important discoveries in this field of study. At the same time, Artificial Intelligence (AI) technologies are already involved in the healthcare industry. The problems that AI resolves now are only tasks-oriented and represent separate tools like recognition of images with malignancies, analyzing and predicting using Big Data, etc.

Methods

This literature review employs a systematic search strategy across academic databases, including PubMed and ScienceDirect, from 2010 to 2023. Inclusion criteria prioritize peer-reviewed articles discussing the integration of artificial intelligence in public health models for cancer care.

Results

We purpose to create an AI oriented public health model in cancer care to follow the patient from his first contact with a doctor till the end of his treatment, maximumly excluding the doctor from this process. For this, we propose to create different AI units. Regional AI (RAI) is designed to manage the patients, interact with other specialists, laboratories and treatment units. General AI (GAI) will collect data from RAI, control and supervise the healthcare process generated by RAI. Another type of AI is Scientific AI (SAI), which is to analyze all data and generate new findings for the scientists in the way to offer new ideas and approaches. Also, it will be used for clinical trials. We propose to change the current developmental process that consists of Human-AI interaction and to add an additional element Teacher AI (TAI). The role of TAI is to modify and sustain the development of AI systems on human demand without involving software engineers and other specialists.

Conclusions

We suppose that the implementation of this model in the healthcare process will decrease the complexity of everyday tasks of oncological specialist by assigning the management tasks to AI, will improve the treatment outcomes by removing human mistakes, and at the same time will decrease the cost of treatment and management in oncology by saving the high quality of cancer care. Human-TAI-AI model of development can decrease the cost of development and implementation for this system.