Editorial Book

Book Title: AI and Robotics in Animal Science

OPEN ACCESS | Published on : 08-Aug-2026 | Pages: 100-111 | Doi : 10.37446/edibook202024/100-111

AI in Disease Diagnosis and Treatment

• Amrita A Vasava
• Assistant Professor, Department of Veterinary Physiology & Biochemistry, College of Veterinary Science & A. H., Kamdhenu University, Sardarkrushinagar, Dantiwada, Gujarat, India.


• Jay A. Vasava
• Teaching Associate, Post Graduate Department of Computer Science & Technology, Sardar Patel University, Vallabh Vidyanagar, Gujarat, India.


• Priti S. Sajja
• Director & Professor, Post Graduate Department of Computer Science & Technology, Sardar Patel University, Vallabh Vidyanagar, Gujarat, India.


• Pankaj A. Patel
• Associate Professor & Head, Department of Veterinary Physiology & Biochemistry, College of Veterinary Science & A. H., Kamdhenu University, Sardarkrushinagar, Dantiwada, Gujarat, India.

Abstract

Artificial intelligence (AI) is rapidly restructuring contemporary healthcare by enhancing disease diagnosis, prediction, treatment planning, and patient management. Integrating machine learning, deep learning, natural language processing, computer vision, and clinical decision support systems, AI enables the analysis of large, heterogeneous medical datasets with high accuracy and efficiency. By processing multimodal information including medical imaging, electronic health records, laboratory data, genomic profiles, and physiological signals AI-assisted systems facilitate early disease detection & improve diagnostic precision. Substantial clinical progress has been achieved across oncology, cardiology, neurology, infectious diseases, ophthalmology, endocrinology, and respiratory medicine, where AI tools assist in tasks such as automated image interpretation, risk stratification, treatment response prediction, and longitudinal disease monitoring. These developments contribute directly to precision medicine by enabling patient-specific treatment strategies, optimized drug selection and dosing, and refined prognostic assessment. Despite these advances, widespread integration of AI into routine clinical workflows remains challenging. Critical issues include data quality and representativeness, algorithmic bias, limited model interpretability, regulatory and ethical complexities, cybersecurity threats, privacy protection, interoperability, and clinician acceptance. Addressing these barriers demands robust validation, transparent and explainable models, standardized data infrastructures, and close multidisciplinary collaboration.

Keywords

Artificial Intelligence, patient management, deep learning, genomic profiles, disease monitoring

References

Abbasi, A., et al. (2022). Machine learning-based diagnosis of COVID-19 using chest X-ray images. Computers in Biology and Medicine, 146, 105587.

Bhatt, J., et al. (2025). Artificial intelligence in healthcare diagnosis: Evidence-based recent advances and clinical implications. Sensors & Diagnostics, 4, 1047–1059.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101

Lancet Infectious Diseases. (2026). Artificial intelligence and infectious disease diagnostics: State of the art and future perspectives.

Nojomi, M., Babaee, E., Rampisheh, Z., Benis, M. R., Soheyli, M., & Rady Raz, N. (2025). AI-powered clinical decision support systems in disease diagnosis, treatment planning, and prognosis: A systematic review. Medical Journal of the Islamic Republic of Iran, 39, 81. https://doi.org/10.47176/mjiri.39.81

Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., Patel, B. N., Yeom, K. W., Shpanskaya, K., Blankenberg, F. G., Seekins, J., Amrhein, T. J., Mong, D. A., Halabi, S. S., Zucker, E. J., ... Ng, A. Y. (2018). Deep learning for chest radiograph diagnosis: CheXNeXt algorithm. PLoS Medicine, 15(11), e1002686.

Sadr, H., Nazari, M., Khodaverdian, Z., Farzan, R., Yousefzadeh-Chabok, S., Ashoobi, M. T., Hemmati, H., Hendi, A., Ashraf, A., Pedram, M. M., Hasannejad-Bibalan, M., & Yamaghani, M. R. (2025). Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: A comprehensive review of machine learning and deep learning approaches. European Journal of Medical Research, 30, 418. https://doi.org/10.1186/s40001-025-02680-7

Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 103(2), 167–175.

Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

World Health Organization. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.

Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z.