| | DECEMBER 20259CIOReviewis fuel for AI, and advances in the health fields like digital medical records, digital imaging (MRIs, TC SCANs, etc.), medical devices, or even other daily standard devices such as your smartwatch, cellphone, gaming goggles, earphones can obtain a lot of your body information and convert it into digital data (heart rate, blood pressure, breathing rate, eye movement, walking speed, walked distance, speech, and even the difference in the sound of your voice, and many other variables that can be measured.). Another critical factor is that many of these gadgets are broadly available to the population. Cell phones have been evolving into tiny portable computers added with several types of sensors and are used by a high percentage of the population, even in low-income countries. This device allows the user to not only access information via the internet but to potentially generate a lot of real-time data. So large cohort's data capture is quite simple now. After being analyzed, all that information can accurately inform you about your vascular health, cognitive status, daily habits, and many other details that we usually don´t pay attention to, allowing health professionals to detect minor anomalies that can represent early manifestations of a disease. Data mining, machine learning techniques, and other AI-related branches and applications allow us to compare, correlate or match biological data obtained from an individual and contrast it to large cohorts databases. At the same time, it allows thorough adjustments to improve the precision with which it is calculated. And this is only just a tiny glimpse of all the potential possibilities that can be applied in the health area. On the other hand, pharma companies are taking advantage of the calculus power of computer hardware and these techniques to increase the chance of success in new treatment developments. Only very few molecules of all studied reach the clinical stage. The chance of successfully obtaining a new treatment after all clinical studies is estimated between 10-20 percent. All this process takes around eight years and several hundred million USD. If we take into account the preclinical stage, the time and resources needed increase significantly.For pharma companies, this technology could significantly reduce the time and resources needed for molecular scouting in the preclinical stages and facilitate to facilitate data gathering and analysis in the clinical stage. Improving the success rate while reducing time and cost will be critical to obtaining cost-efficient treatments with new technologies such as antisense oligonucleotides (ASOs), Gene therapy, or CRISP editing, as well as other more used technologies like monoclonal antibodies or synthetic drugs.In the case of medical practice, it won´t replace physicians for sure. Still, it would allow adding a lot of information and details that are not usually processed during daily practice. Allowing earlier diagnosis, more accuracy in diagnosis, and better follow-up of disease progression and treatment results. Physicians will need to learn how to work with these tools and how to get the most out of them. Also, patients will notice changes in how their treatment options can be adjusted with real-time information. And all of this is just the tip of the iceberg.We have entered on a one-way road, where is no possible turning back. AI will be present everywhere. Our jobs will be impacted by it, reducing many long hour-consuming tasks making some skills obsolete, and creating the need for other types of skills more aligned with AI.
< Page 8 | Page 10 >