Use of machine learning in the medical field

Introduction: Currently, technology is a constant presence in our daily lives. Machine Learning (ML) is an artificial intelligence technique that allows machines to learn from data and experiences, programmed using algorithms trained by artificial intelligence, the technology is able to recognize patterns and generate immediate solutions. ML is a revolutionary field of medicine, with enormous resources being applied to fusing computer science and statistics into medical problems. This artificial intelligence is the future of personalized medicine, as it can lead to more accurate diagnostic algorithms and individualize patient care. Objective: In the present study we explore the use of Machine Learning in the medical field and the future potential of ML in medicine. Methods: This was a descriptive, qualitative, cross-sectional, retrospective literature review study based on PubMed data over the last 10 years, with the following inclusion terms: machine learning, advances in medicine, machine learning in medicine. Results: The results obtained indicate that the use of Machine Learning in medicine offers advantages, such as improving diagnostic accuracy and clinical treatment guidance, considering the limitations of the technology and models trained before use in patients, such as the scarcity of validated data and the lack of consensus on which features are relevant to each clinical problem. In the field of medical imaging, advances in Machine Learning have been applied in the diagnosis of pathologies through radiographic images. There was also the application in histopathological images, which has been explored to infer important genomic characteristics, which can help predict the response to therapies and the prognosis of patients with cancer. Conclusion: The set of our data shows that these advances show the potential of Machine Learning in the most diverse medical areas, but it is necessary to continue improving the algorithms and carrying out additional research to guarantee its effectiveness and reliability.