# Artificial intelligence-driven screening, early diagnosis, and treatment strategies for cervical cancer: an overview

## TL;DR

This paper reviews how artificial intelligence improves cervical cancer screening, diagnosis, and treatment, offering more accurate and efficient methods compared to traditional approaches.

## Contribution

The paper provides a comprehensive overview of AI applications in cervical cancer, highlighting novel uses in biomarker prediction and personalized treatment strategies.

## Key findings

- AI-powered screening methods like automated Pap smear analysis improve accuracy and efficiency.
- Machine learning identifies biomarkers such as microbial and metabolic fingerprints for early diagnosis.
- AI-assisted drug development uncovers new therapeutic targets and chemotherapy regimens.

## Abstract

Cervical cancer (CC) is a significant global health issue, particularly in low-income countries. Early detection and successful treatment techniques are critical for decreasing death rates. Artificial intelligence (AI) has emerged as one of the major transformational tools in cancer prediction, improved screening, diagnosis, and therapy. This comprehensive narrative review describes applications of AI in diagnosis, biomarker prediction, development of drugs, and tailored treatment for cervical cancer. Various studies reporting the use of AI-driven imaging methods, multi-omics data analysis, and deep learning algorithms were evaluated for their influence on enhancing CC treatment. AI-powered screening approaches, such as automated Pap (Papanicolaou) smear analysis and colposcopy interpretation, outperformed traditional techniques in terms of accuracy and efficiency. Machine learning algorithms helped in identifying crucial biomarkers, such as microbial and metabolic fingerprints, which improved early diagnosis. AI-assisted drug development has resulted in the identification of new therapeutic targets and improved chemotherapy regimens. Personalized medical techniques, based on AI-driven multi-omics data analysis have helped to increase patient outcomes. However, issues including dataset constraints, clinical validation, and ethical considerations must be resolved before the broad adoption of AI-based diagnosis and therapy. Future research should focus on improving AI models and incorporating them into clinical practice to improve CC management.