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@article{182073,
        author = {Radhe Shyam and Rakesh and Prateek and Nidhi and Shefali Madan},
        title = {Revolutionizing Healthcare Through Machine Learning And Artificial Intelligence: Challenges, Applications, And Future Prospects},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {771-779},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182073},
        abstract = {Image processing has now become more intelligent and transformed into high-tech machine learning technology in healthcare, which manipulates the patients into a new world of precision, efficiency, and innovation in their automation. In that, with the use of computational technologies, many aspects are influenced and have demonstrated improved disease diagnosis, personalized treatment planning, clinical decision-making, operational management, etc. There are AI-driven tools and ML algorithms that hold great potential in extracting hidden patterns from enormous and complex medical data and generating a lot of insights that could go beyond the traditional clinical methodologies.
The overall understanding within this paper has been synthesized from a number of scholarly studies and recent literature to provide a handy and systematic overview of the many applications found in healthcare involving the use of ML and AI. The documents thus critically interrogate AI use in the contexts of the medical imaging aspect, predictive analytics, patient monitoring, electronic health record management, and precision medicine development. Besides, it would recognize the revolutionary capabilities of AI technologies-in addition to reviewing and discussing the possible challenges and drawbacks of the technology that would inhibit their wider applications. These include the ethical considerations regarding the use of patient data, interrogation into issues of privacy and security, presence of biased algorithms, and the typical opaque nature of AI decision-making models.
The paper thus aims to fill the gap born between innovative technology and possible life in healthcare delivery by doing an in-depth analysis along with a conceptual framework of ethical as well as effective application of AI and ML in clinical settings. The ultimate objective is to guide healthcare professionals, policymakers, and researchers on using AI-enabled solutions in further improvement of diagnostics accuracy, better patient outcomes, and therefore a more efficient, clear, and patient-centered healthcare ecosystem.},
        keywords = {},
        month = {July},
        }