Artificial intelligence in healthcare neural network ethics of machine learning transformative impact| International Journal of Innovative Science and Research Technology

Artificial Intelligence in Healthcare: Neural Network, Ethics of Machine Learning, Transformative Impact

Authors : Neha Khatri; Bhanupriya Thakur; Yash Rautkar; Bhaskar Jha

Volume/Issue : Volume 10 - 2025, Issue 8 - August

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DOI : https://doi.org/10.38124/ijisrt/25aug197

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Abstract : Artificial intelligence (AI) is transforming healthcare with advanced diagnostics, personalized medicine, and improved patient outcomes. This article explores the applications of neural networks and machine learning for diagnosing and controlling tongue cancer and brain Hemorrhage. The ethical aspect of embracing AIinclinical practice is alsodiscussed. The debate intertwines existing research, emphasizes clinical breakthroughs, and outlines challenges and directions. Recent studies have demonstrated that convolutional neural networks (CNNs) are capable of competing with the diagnostic accuracy of seasoned radiologists in medical imaging modalities such as MRI, CT, and PET scans. In brain Hemorrhage, AI-based systems have produced promising results with real-time detection, enabling faster emergency response time and timely surgical intervention. For tongue cancer, AI has enabled more efficient screening using histopathological image analysis and oral scans, which assist doctors in staging and grading tumors more consistently. This study reviews current literature and clinical case reports to draw attention to the potential for AI to revolutionize precision medicine and public health. It concludes with recommendations for future research, including the need for longitudinal clinical trials, federated learning algorithms to protect patient data, and inclusive AI systems that are generalizable to heterogeneous populations.

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Artificial intelligence (AI) is transforming healthcare with advanced diagnostics, personalized medicine, and improved patient outcomes. This article explores the applications of neural networks and machine learning for diagnosing and controlling tongue cancer and brain Hemorrhage. The ethical aspect of embracing AIinclinical practice is alsodiscussed. The debate intertwines existing research, emphasizes clinical breakthroughs, and outlines challenges and directions. Recent studies have demonstrated that convolutional neural networks (CNNs) are capable of competing with the diagnostic accuracy of seasoned radiologists in medical imaging modalities such as MRI, CT, and PET scans. In brain Hemorrhage, AI-based systems have produced promising results with real-time detection, enabling faster emergency response time and timely surgical intervention. For tongue cancer, AI has enabled more efficient screening using histopathological image analysis and oral scans, which assist doctors in staging and grading tumors more consistently. This study reviews current literature and clinical case reports to draw attention to the potential for AI to revolutionize precision medicine and public health. It concludes with recommendations for future research, including the need for longitudinal clinical trials, federated learning algorithms to protect patient data, and inclusive AI systems that are generalizable to heterogeneous populations.