• Artificial Intelligence in Cancer Diagnosis : Advances and its role in biotechnology Medicine
  • Farzaneh Alemohammad,1,* Fatemeh RezaGholi,2 Pouria Rostampur,3
    1. Department of Medical Biotechnology , School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa , Iran
    2. Department of Medical Biotechnology , School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa , Iran
    3. Department of Medical Biotechnology , School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa , Iran


  • Introduction: Introduction Cancer is a complex disease where timely diagnosis plays a pivotal role in determining treatment success and improving patients' quality of life. Traditionally, diagnosis involves clinical examinations, laboratory tests, medical imaging, and biopsies. Early detection remains a critical factor in enhancing survival rates. Recent advancements in molecular biology, including next-generation sequencing (NGS) and molecular analysis, have enabled faster and more precise identification of various cancers. These methodologies utilize genetic and molecular tumor profiling to facilitate personalized treatment decisions. Additionally, the application of advanced imaging techniques and automated medical image analysis significantly improves diagnostic accuracy while reducing human error. Overall, these modern biotechnological approaches contribute to faster diagnoses, more effective treatments, and decreased mortality from cancer. Transformation of Cancer Diagnosis Through Artificial Intelligence Remarkable strides in biotechnology and technology, particularly the rise of artificial intelligence (AI), have revolutionized cancer diagnosis. AI excels at analyzing large and complex datasets, enabling doctors to make quicker, more accurate, and highly sensitive diagnoses. AI is extensively used in medical image analysis as well as in processing molecular and genetic data. It assists in identifying biological markers and predicting disease progression, thus playing a crucial role in personalizing treatment according to patients’ unique characteristics. With the development of deep learning algorithms and complex neural networks, AI now enhances the interpretation of medical images such as mammograms, MRI scans, CT scans, and biopsy samples. These advances not only support clinicians in diagnosis but also improve patient treatment outcomes by guiding more precise therapeutic decisions.
  • Methods: Computer-Aided Diagnosis (CAD) in Medical Imaging Artificial intelligence has sparked a revolution in interpreting medical images such as mammograms, CT scans, MRIs, and pathology slides. Deep convolutional neural networks (CNN) and other deep learning models have demonstrated sensitivities and accuracies surpassing those of human experts. For instance, AI-driven systems have achieved over 96% accuracy in breast cancer detection and about 87% sensitivity in diagnosing lung cancer. Whole Slide Imaging (WSI), combined with AI assistance, enables more accurate pathology analysis, helping improve tumor staging and disease progression prediction. CAD systems automate the processing and analysis of medical images to assist radiologists and doctors in identifying anomalies such as tumors or suspicious regions with higher precision. The workflow involves image preprocessing to reduce noise and distortion, pattern recognition to differentiate between normal and abnormal tissue, and localization of regions of interest that may indicate disease. CAD provides additional information about lesion size and morphology and supports early diagnosis by offering helpful suggestions to clinicians. Although CAD has been used clinically for over four decades—primarily as a supplementary decision-support tool—recent advancements in AI and deep learning have enabled fully automated, high-precision diagnostic capabilities. This progress improves healthcare quality and reduces cancer detection times. Integration of Multi-omics Data and Biomarker Discovery Artificial intelligence also plays a crucial role in integrating data from multiple omics disciplines such as genomics, epigenetics, transcriptomics, and proteomics. By analyzing these complex, high-dimensional datasets, AI models trained on large databases like The Cancer Genome Atlas (TCGA) provide multifaceted predictions and personalized insights for cancer diagnosis and treatment. These models not only forecast treatment responses but also enable precise medical personalization by analyzing molecular data. For example, deep learning frameworks like DeepKEGG design biological modules to uncover complex communications between multi-omics data, identifying markers linked to cancer recurrence more effectively than traditional methods. Integration of DNA methylation data, miRNA expression, and gene copy number variations (CNVs) provides optimal features for predicting patient survival and response to therapy in various cancers. Advanced machine learning algorithms enhance the understanding of molecular pathways common among different cancers and help identify therapeutic targets. This integration improves the accuracy of diagnosis, prognosis, and cancer treatment strategies, opening new horizons in biotechnology and precision medicine. AI-Driven Personalized Modeling and Screening Artificial intelligence combines clinical information, molecular profiles, and medical imaging to accurately predict cancer risk, treatment outcomes, and disease progression on an individual level. These comprehensive models support personalized screening programs that significantly reduce unnecessary biopsies and medical interventions. For instance, AI can predict pathological responses to neoadjuvant chemotherapy in triple-negative breast cancer with about 90% accuracy. Deep learning models also excel in short-term breast cancer risk estimation, outperforming traditional clinical approaches by identifying more patients at risk. In lung cancer, AI models integrating CT and clinical data enhance prediction accuracy, optimizing screening to focus on high-risk patients and reducing unnecessary testing. This personalized screening approach improves care quality, lowers treatment costs, and enables earlier clinical interventions, leading to better prognoses. Innovations in AI for Cancer Diagnosis Recent AI innovations include deep learning-powered tools that automate metastasis identification, microtumor microenvironment analysis, and patient survival prediction from digital pathology images with greater precision and speed. Multimodal AI models integrate data from radiology, histology, and genomics, providing comprehensive patient profiles and significantly improving diagnostic accuracy. AI-based remote diagnostic technologies are expanding access to specialized pathological services in underserved and remote areas, helping to reduce healthcare inequalities. Furthermore, AI plays a vital role in optimizing immunotherapy and nanomedicine treatments by selecting materials and designing targeted therapies that increase effectiveness and minimize side effects. Despite impressive advancements, AI in cancer diagnosis faces several challenges: The need for large volumes of diverse, high-quality data to build strong and generalizable models
  • Results: Challenges in the Application of AI Despite impressive advancements, AI in cancer diagnosis faces several challenges: The need for large volumes of diverse, high-quality data to build strong and generalizable models. Potential algorithmic biases from imbalanced datasets, which may affect accuracy across different populations. Ethical concerns related to patient privacy, data security, and informed consent. Limited interpretability of many deep learning models, often perceived as “black boxes,” which hinders clinician trust and acceptance. Addressing these challenges requires interdisciplinary collaboration among AI experts, clinicians, policymakers, and patients. Future Perspectives Ongoing research focuses on improving AI model quality through large-scale clinical validation, enhancing model interpretability, and ensuring seamless integration into clinical workflows. Combining AI with advanced technologies like next-generation sequencing and multi-omics profiling will further enhance the precision of cancer diagnosis and therapy. Smart medicine systems capable of real-time patient monitoring and continuous status assessment promise earlier intervention and better management of disease progression
  • Conclusion: Conclusion Artificial intelligence has emerged as a transformative force in biotechnology and medicine, significantly enhancing the accuracy, speed, and personalization of cancer diagnosis. By employing advanced algorithms and integrating diverse data—including medical imaging, molecular, and clinical information—AI enables early detection, optimized screening, and tailored treatments. Although challenges remain, continued interdisciplinary collaboration and technological innovation signal a bright future for AI-guided cancer care. This promise includes reduced cancer mortality rates, superior patient outcomes, and improved global healthcare delivery.
  • Keywords: Artificial Intelligence, Cancer Diagnosis, Medical Imaging, Deep Learning, Multi-omics Integration,