Inteligência artificial aplicada à saúde: Qualidade na busca de diagnóstico

Abstract

This study investigates the application of Artificial Intelligence (AI) in healthcare, focusing on improving the quality of medical diagnoses through image analysis. Using Alzheimer's disease as a case study, the research explores the functioning of self-learning algorithms and proposes an alternative solution to assist medical diagnosis. Data mining and deep learning methods were employed to enhance diagnostic accuracy, especially in simple cases. Convolutional Neural Networks (CNNs) were used in the analysis of medical images, such as MRIs and X-rays, highlighting the role of deep learning in more accurate diagnoses and predicting patients' future health. The results indicate that AI has great potential to support and, in some cases, replace medical diagnosis in less complex situations, while more robust algorithms are needed for more complex diagnoses. However, for this integration of AI in medicine to become a reality, investment in infrastructure, technology development, and education is crucial. Additionally, the protection of intellectual property for new innovations must be ensured. It is concluded that AI will not replace doctors but will complement their activities, enhancing diagnostic power by combining human decision-making abilities with AI's capacity to analyze large amounts of data. The balance between humans and machines is essential for a promising future in diagnostic medicine.