Health, the new challenge in Artificial Intelligence

Data has significantly grown with the advent of network devices in the health sector such as medical clinical histories, diagnostic processes, and more particularly, medical imaging -the introduction of Real-World Evidence. Making correct use of this data could save a great number of lives and reduce sanitary costs.

Currently, various sanitary systems are at a turning point in providing better services at lower costs due to changes in the population growth, among other aspects. On the other hand, the pharmaceutical industry needs more efficient and precise drugs research through targeted therapies aiming at reducing secondary effects and save lives.

Technological advances in Artificial Intelligence, artificial neural networks, advanced analytics based on Machine and Deep Learning techniques and Big Data technology, which enable management of huge data volume either structure or unstructured, are finding new ways to advance in health science as never seen before. Artificial Intelligence has been put at the service of health care and the citizens.

But where is this Artificial Intelligence used in health care? Although the use of artificial intelligence has indeed improved doctors´ productivity and clinical results, there is still a long way to go (5 years approximately). Clinical applications based on AI are rather rare so far.

We will show you the health care fields Artificial Intelligence is used in:

  • Anomalies Detection: Medical problems or textual errors can be identified and marked. Currently, image analysis efficiency has been improved by marking specific anomalies for image technicians´ examinations which help them to prioritize anomalies analysis, saving time and improving patients´ assistance. Moreover, anomalies detection is also used to reduce health insurance fraud through anomalies behavior and patterns analysis of insured people.
  • Robot-assisted surgery: Artificial intelligence has substantially improved surgeries outcomes thanks to preoperative data analysis which helps doctors to update new surgical techniques, reduce complications and hospital stay. Da Vinci Robot is a good example that has been successfully used by the sanitary system in Spain.
  • Accurate dose prescription: Knowing the exact dose prescription of every drug for each patient is complicated and involves patterns and assumptions extracted from clinical tests. Wrong drug administration accounts for 3% of medical errors. This can be drastically reduced by using improved data analysis. It is precisely in this field where Real World Evidence or siRNA design tools become decisive.
  • Virtual assistance: Health institutions, hospitals, etc., use AI virtual nursing assistants to ask patients questions about their health, symptoms, medication and give them recommendations. This reduces significantly the unnecessary hospital visits, saving medical practitioners´ time to focus on more critical cases.
  • Management Efficiency: Sanitary management costs are very high. Artificial Intelligence can fix and improve existing flaws, improve workflows and eliminate or reduce time-consuming routine tasks such as tests and prescriptions requests. Automation reduces the specialists´ workload to prioritize urgent cases.
  • Malignant cells Identification: Currently, surgeons depend on biopsies to know what tissue to remove, consequently, the identification and analysis processes are crucial. Al devices provide thousands of cancer cells images and patients cases that feed the AI automatic learning which will improve accuracy in malignant cells identification.
  • Helping in disease prevention: AI has made a significant leap forward in the health care sector thanks to the IoT and health applications. These network devices help people to keep healthy by the health recommendations given and lead to space out hospital visits. The applications also help doctors to understand daily patients’ behaviors.
  • Training:  In medical students and nursing training, computers with AI applications are used for scenarios simulations challenges which would not be possible to do in real life. This training can also be carried out in many other platforms such as telephones, tablets, computers…

As we have seen throughout this post, Artificial Intelligence is getting more and more advanced, are costs saving and helps to carry out tasks faster and more efficiently than no one else. The tool possibilities are infinite in the health field as shown in the above examples which are just a few along the path we have just begun.

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