1. Introduction
Background: Diabetes, cancer, cardiovascular illness, and other non-communicable diseases (NCDs) represent significant global health challenges, contributing to high mortality rates and economic burdens. Early detection and timely intervention are critical for managing these conditions effectively. Patients are strongly advised to undergo regular screenings, as early diagnosis significantly improves treatment success and overall prognosis. In this context,Artificial Intelligence (AI) has emerged as a transformative force in the healthcare industry. AI serves as a powerful diagnostic tool, capable of analyzing complex medical data, such as imaging, lab results, and genetic information, with remarkable speed and precision. Its integration enables healthcare providers to identify diseases in their earliest stages, often before symptoms become apparent.

Thesis Statement: Technology has a critical role in delivering early diagnostic solution of chronic diseases with better precision and time effectiveness. But to reach this potential, issues around data quality and ethical use, and integration into healthcare settings need to be overcome. (Badidi, 2023)

Scope and Purpose: This review assesses AI’s strengths and weaknesses in early disease diagnosis, with the aim of establishing best practices for integrating AI into clinical workflows. By analyzing the current state of AI in healthcare, the review highlights its potential in improving diagnostic accuracy, speed, and accessibility, especially in areas with limited healthcare resources. However, it also addresses the challenges AI faces, such as data quality issues, algorithmic bias, and ethical concerns. The review explores the future of AI developments in healthcare, discussing advancements in machine learning, integration with medical devices, and the role of AI in personalized medicine.

2. Findings & Analysis
A) opinions on the improvements in the diagnostic ability.
Findings: The deep machine learning-based models overcome conventional diagnostic approaches concerning diseases in their early stages, including cancer. (Khandakar, Al Mamun, Islam, Hossain, Melon, & Javed, 2024)

Analysis: AI: Accurate diagnosis is achieved through the analysis of vast volumes of medical images as well as patient’s record database. However, its effectiveness is only limited by the quality of the training data. It is a fact that preconceptions in databases are problematic for diagnostic results with residents of some regions or minorities in particular.

B) Speed in Identification
Findings: Various integrated diagnostic systems that use artificial intelligence such as the IBM Watson reduce the duration that is taken to screen data through analysis than any other human being

Analysis: However, increased efficiency is a strong advantage, despite the fact that the use of AI in such cases may decrease clinical judgments. It is necessary to combine the machine learning outcomes with a health care professional’s view in order to cover all facets. (Kadayat, Sharma, Agarwal, & Mohan, 2024)

c) Personalized Medicine
Findings: Due to the possibilities offered by artificial intelligence, patients have individual diagnostic strategies based on genomic, environmental, and lifestyle data (artificial intelligence)

Analysis: Pharmacogenomics enhances treatment features of an individual patient based on patient characteristics. However, the large amount of data that needs to be collected can be burdensome in terms of privacy and data security, which means they need to be protected aggressively. (Zahra, Al-Taher, Alquhaidan, Hussain, Ismail, Raya, & Kandeel, 2024)

D) Implications of ethical and Legal Nature
Findings: Ethical issues, such as data misuse and lack of transparency in AI decision-making, are significant barriers. Several challenges were identified and among those, ethical issues are the biggest problem

Analysis: Accountability and interpretability of the AI systems, remain paramount crucial. These problems can be solved by, using ethical frameworks and using transparent algorithms to improve the trust of the healthcare providers and the patients. (Herington et al., 2023)

3. Conclusion
Summary of Key Findings:

Including early diagnosis of chronic diseases has been improved using AI because of accuracy, efficiency, and customization. But data biases, ethical questions and integration issues restrain this general adoption.This review establishes AI’s impact on healthcare as revealed by yellow literature and increasingly negative in terms of its ethically unregulated, financially inequitable, and endangering capabilities. Still, future investigations should be designed to raise the accuracy of AI and overcome the identified drawbacks.

There is an impending need to address issues relating to the acquisition of a balanced dataset, applying AI in regions that lack resources, and ensuring AI is clear and works alongside clinicians.An adoption of AI in chronic diseases’ management requires healthcare stakeholders to concentrate on regulatory and policy environments, interdisciplinary and interprofessional collaboration, and patients and personalized care. It is possible to help governments adapt AI for the better and help various populations worldwide get better medical assistance.

2. Reference
Badidi, E. (2023). Edge AI for early detection of chronic diseases and the spread of infectious diseases: opportunities, challenges, and future directions. Future Internet, 15(11), 370. 9
Khandakar, S., Al Mamun, M. A., Islam, M. M., Hossain, K., Melon, M. M. H., & Javed, M. S. (2024). Unveiling Early Detection And Prevention Of Cancer: Machine Learning And Deep Learning Approaches. Educational Administration: Theory and Practice, 30(5), 14614-14628.
Kadayat, Y., Sharma, S., Agarwal, P., & Mohan, S. (2024). Internet-of-Things Enabled Smart Health Monitoring System Using AutoAI: A Graphical Tool of IBM Watson Studio. In Communication Technologies and Security Challenges in IoT: Present and Future (pp. 427-445). Singapore: Springer Nature Singapore.
Zahra, M. A., Al-Taher, A., Alquhaidan, M., Hussain, T., Ismail, I., Raya, I., & Kandeel, M. (2024). The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease. Drug Metabolism and Personalized Therapy, 39(2), 47-58.
Herington, J., McCradden, M. D., Creel, K., Boellaard, R., Jones, E. C., Jha, A. K., … & Saboury, B. (2023). Ethical considerations for artificial intelligence in medical imaging: deployment and governance. Journal of Nuclear Medicine, 64(10), 1509-1515.