The arrival of Artificial Intelligence to many sectors has allowed its professionals to improve and accelerate the diagnoses of a large number of pathologies. But it has also allowed researchers in the health sector to make advances in research that seemed impossible just a few years ago. Furthermore, having specialized support not only for the diagnosis, but also for the subsequent phases, has given medical professionals the opportunity to reach goals that were unthinkable until very recently.
In disease detection and diagnosis, the use of machine learning It has allowed us to observe, collect and analyze data and vital signs of patients in a multitude of situations. From those who are in intensive surveillance units, to those who communicate data on some aspects of their health periodically from their homes or with analyzes from time to time. Based on this information, they are responsible for detecting and alerting about possible risk factors.
On the other hand, after a diagnosis, AI can also intervene in the treatment of different pathologies, with the aim of personalizing it so that it is more effective. Thanks to the support of Artificial Intelligence, medicine can be more precise and effective. As AI models can learn the clinical history and even personal circumstances of each patient, they could offer personalized recommendations in real time.
AI as support in the analysis of medical images
Currently, however, where the influence of AI in medicine is being most noticeable is in everything related to obtaining medical images: scanners, computed tomography, magnetic resonance imaging, x-rays, etc.
According to various research, AI can become one of the most effective tools to confirm certain diagnoses, such as breast cancer, or improve diagnoses of all types of diseases, making progress in the early detection of serious, degenerative or disabling diseases.
AI also serves to improve the efficiency of clinical trials, for example by streamlining systems responsible for assigning medical codes to patient results and speeding up the updating of relevant data sets. Regarding the development and discovery of new medicines, perhaps one of the areas in which research takes the longest and costs the most money, AI can help reduce both the costs of developing new drugs and the time spent obtaining them.
Integrating AI into clinicians’ workflows can also provide valuable context for decision-making. Thus, having an algorithm machine learning Specifically trained can reduce the time it takes to investigate a disease, providing doctors with search results supported by evidence-based information about treatments and procedures.
Fewer errors, more security and lower costs
On the other hand, as is also the case in other fields, the intervention of AI in medicine can greatly reduce errors made by humans. Decision-making driven by intelligent algorithms can avoid errors when, for example, prescribing the most appropriate medication for a specific treatment.
In the future, patients could also have more means to receive information about pathologies if the health care they receive incorporates chatbots that they can access at any time of the day to receive answers to basic questions and doubts, as well as to receive specific information. about your treatment or other aspects of your history. This way they would avoid having to wait for their doctor to be available for in-person or telephone consultations. It could even be used in certain scenarios to speed up triage and to save various information for later review, for example in situations that affect the health of the general population.
The possibilities that Artificial Intelligence opens up for medicine are practically endless, and are expanding almost every day. Thanks to it, for example, lung lesions caused by COVID-19 have been detected more reliably, thanks to a 3D image segmentation system from computed tomography scans powered by deep learning and other AI tools.
Of course, in order to incorporate AI into medicine, as well as other sectors where its role is of vital importance, it is necessary to have equipment and systems capable of executing and managing workloads. In “Connected Healthcare: from data management to Artificial Intelligence,” we analyze four of the technical challenges facing healthcare organizations and how the new HPE ProLiant Gen 11 servers with AMD processors can offer them the best answers. Do not miss it!
