Revolutionizing Oncology: Harnessing Artificial Intelligence for Precision Tumor Detection and Personalized Treatment with Microbiological Insight
The inclusion of artificial intelligence (AI) in oncology has transformed the identification and treatment of malignancies, giving remarkable accuracy and individualization. Using microbiological techniques like metagenomic sequencing, 16S rRNA sequencing, and microbial biomarker analysis, this paper explores the new uses of artificial intelligence (AI) in enhancing diagnostic accuracy, fine-tuning treatment regimens, and predicting outcomes in certain malignancies. Advanced imaging, deep learning (DL), and machine learning (ML) may improve early identification, tumor characterization, and treatment planning. The study combines recent advances, offers creative AI-based solutions, and addresses ethical concerns, model interpretation, and data quality. Two novel visualizations a bar chart and a line graph, depict the benefits of AI on diagnostic sensitivity and microbial biomarker prediction accuracy, integrating microbiological data. Directions for the future stress multi-omics and microbiome integration with big language models to assist individualized cancer treatment, enabling fair access to AI-enabled solutions.
Authors
- A. H. Tariq (ORCID: https://orcid.org/0009-0002-6098-9511)
- Zainab Nadeem
- Muhammad Arif (ORCID: https://orcid.org/0000-0003-0359-5161)
- Syed Khalid Mustafa (ORCID: https://orcid.org/0000-0002-1157-9447)
- Muhammad Faizan (ORCID: https://orcid.org/0000-0001-5570-242X)
- Arif Hussain (ORCID: https://orcid.org/0000-0001-8485-4406)
- Maliha Ghaffar
- Muhammad Umer Chorahi
- Lubna Shabbir
Institutions
- University of Okara (PK)
- Superior University (PK)
- University of Veterinary and Animal Sciences (PK)
Publication Details
- Journal
- Indus journal of bioscience research.
- Published
- 2025-06-30
- DOI
- https://doi.org/10.70749/ijbr.v3i6.1710
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00