The use of Artificial Intelligence, or AI, is becoming increasingly popular in the field of medicine, particularly in diagnosis and treatment management. AI can help identify complex patterns in diagnostic data to detect diseases earlier and improve treatments. It can also recognize specific demographic or environmental areas where high-risk diseases or behaviors are common. In addition, machine learning techniques are used to identify patterns and anomalies in electronic medical records and to carry out ad hoc evaluations of data collected on portable health monitoring devices to perform deep longitudinal phenotyping. In recent years, substantial progress has been made in the automated classification of images, even reaching superhuman levels in some cases.
Examples of successful applications or promising approaches range from the application of pattern recognition software to preprocess and analyze digital medical images, to deep learning algorithms for the classification of subtypes or diseases, digital twin technology and in-silico clinical trials. The potential of AI-based applications is practically endless, but what is the current state of AI in hematology and how far can it go? AI algorithms not only detect details that could escape the human eye, but they could also identify completely new ways of interpreting images. With the introduction of high-performance next-generation sequencing in molecular genetics, the amount of available information increases exponentially, preparing the field for the application of machine learning approaches. The goal of all approaches is to enable personalized and informed interventions, improve treatment success, improve the timeliness and accuracy of diagnoses, and minimize technically induced misclassifications. AI can also help reduce diagnostic workload without compromising the detection of urinary tract infections. Furthermore, intelligent machines raise issues of responsibility, transparency and permission, especially in automated communication with patients. The left side represents the different domains of supervised learning, from artificial intelligence to machine learning and, finally, deep learning.
This section provides information on the relationship between the keywords artificial intelligence and healthcare. Machine learning, a subfield of artificial intelligence, has become a powerful tool for solving complex problems in various fields, including medical diagnosis. For example, researchers have discovered that AI plays a role in the accuracy of diagnosis and helps analyze health data by comparing thousands of medical records, experimenting with machine learning with clinical alerts, efficiently managing health services and care facilities, and allowing patient records to be reconstructed from this data. Genomic biomarkers to predict resistance to hypomethylating agents in patients with myelodysplastic syndromes using artificial intelligence. Artificial intelligence substantially supports the analysis of chromosomal bands, maintaining its strengths in hematological diagnosis, even in the era of newer technologies. Digital data is also a basic prerequisite for the application of emerging artificial intelligence (AI) techniques.
Comparison of interpretations of chest radiography using an artificial intelligence algorithm with those of radiology residents. In conclusion, AI is becoming an indispensable tool in healthcare. It can help identify complex patterns in diagnostic data to detect diseases earlier and improve treatments. In addition, machine learning techniques are used to identify patterns and anomalies in electronic medical records and to carry out ad hoc evaluations of data collected on portable health monitoring devices to perform deep longitudinal phenotyping.
