Algorithms may be hidden, but artificial intelligence and IoT are improving healthcare for patients and doctors in many ways.
One of the sectors that can benefit most from the wave of artificial intelligence (AI) is that of Cheers. Technology is able to generate data, teach how to treat a disease, and put people at the center of their health management. Prevention, early detection of disease and identification of the most appropriate treatments are other advantages.
Artificial intelligence (AI) is an unstoppable force for change in health care. Almost every day there is progress demonstrating that AI and machine learning (IA / ML) will help us better diagnose and predict disease, discover new drug therapies, and provide the most accurate information for doctors to improve service delivery. Cheers.
While these ambitions may be possible, thorny challenges stand in the way of a true AI-enabled health system.
And what can AI do for the patient and health care?
It is important to remember that AI is not magic (or robots that replace your doctor) & #8211; It's just math applied to life.
Terms like #8220; machine learning & #8221; and #8220; deep learning & #8221; are simply ways of explain statistics-based computer algorithms.
And the algorithms are everywhere. They operate in the stock market, decide if you can get a mortgage and one day they can drive your car. They search the internet, show carefully chosen ads on the websites you visit, and decide what prices to show at online stores. An algorithm is essentially a brainless way of doing smart things. It is a set of precise steps that do not require much mental effort to follow, but which, if obeyed exactly and mechanically, will lead to the desired result.
These algorithms need much data to identify patterns and become powerful predictive tools. For example, you would need a lot of patient data to train an algorithm to recognize sepsis markers and use them to predict sepsis in future patients. While more healthcare data than ever is available through electronic health records, remote monitoring tools, and genomic testing, data still tends to be isolated, messy, and proprietary. Patients end up benefiting if researchers can extract patient data from around the world rather than isolated data for a single institution & #8211; and AI can help spur collaboration for scalable impact. As Google Ventures partner and medical scientist Dr. Vineeta Agarwala noted, “AI is uniquely capable of forcing data silos to break and forcing large institutions that historically had legitimate fears of data sharing,
As an investor in early-stage healthcare technology, we see many startups leveraging AI / ML. From 2011 to 2017, investors poured US $ 2.7 billion into AI / ML digital health startups. While media attention has focused largely on IA / ML's ability to revolutionize clinical care delivery, technologies are transforming the healthcare business even faster. Companies are creating more efficient processes, from the pharmaceutical drug development pipeline to clinical operations, hospital scheduling and documentation. Although exciting, we find that some of the more clinically focused use cases & #8211; as robotic diagnosis and treatment & #8211; will take longer to build and scale because of the risk of integrating new technologies into direct patient care.
Why should you care?
How can all this look to you? Although the algorithms may be hidden, AI has probably touched some of your care. Many consumer products integrate AI to offer tailored recommendations. For example, symptom checking applications such as Babylon and Ada are powered by machine learning algorithms, while Woebot provides emotional support to people through an AI technology chatbot. Other solutions help providers manage their patients. For example, Omada Health uses machine learning algorithms to help health coaches predict which of their pre-diabetic patients could use additional support to adhere to their nutrition and fitness routines. And many hospitals use predictive algorithms & #8211; as offered by AgileMD & #8211; to monitor patients and flag emerging clinical problems.
We live in a world with an unprecedented and growing mass of healthcare data collected at home and on the go, from our phones, wearables, sensors and voice tools. To capitalize on this wealth of information, we will need AI / ML to search the data, find patterns, and gain insights that can be used to improve health. As investors and digital health enthusiasts, we look forward to supporting companies applying AI / ML to create a more efficient, affordable and intelligent healthcare system.
They are, in fact, based on symbolic models of nosological essence and relate to the factors linked to the patient and their clinical manifestations. Nosology being the science that deals with the classification of diseases.
Currently, the diagnosis has received less attention regarding the computational and technological support to perform it.
Despite being the focus of many researches, the specialist systems of diagnostic medicine are currently focused only on:
- Educational environments for disease control and alerting;
- Intensive care medicine environments.
Past the initial excitement surrounding the promise of the creation of diagnostic support technologies in the last 10 years has been somewhat disappointing with regard to this technology. Even after having proven their effectiveness and credibility at numerous times, little has evolved.
Much of this “disappointment” relates to the inadequate way in which artificial intelligence systems have been adapted to clinical practice, and the institutions' lack of awareness of the real benefits of this technology, when properly executed.
Challenges and scenario in Brazil
A challenge for artificial intelligence developers is to correctly characterize the medical points that need these systems most.
In Brazil, we can find programs based on artificial intelligence in some institutions such as the Israeli Hospital Albert Einstein, where there are imaging devices that can point out possible diseases and notify the doctor automatically.
In addition to what we mention, they have equipment that sends patient vital signs directly to medical records, among other advanced features.
Nevertheless, AI in Brazil should still take time to grow! In 2016, the government invested R$67 million in three supercomputers that increase 10x the ability to store SUS data.
It is likely that the machines will not be fully used, according to the Ministry of Health, less than half of the country uses the data and care information registration program at the Primary Health Care Units. In addition, some AI systems work only after personal registration and of each doctor in the institution.
This computerization in SUS would allow access to a large database and contribute to the advancement of AMI. Unfortunately for the already known lack of structure the project will not achieve great results.
Fortunately, the tendency is that over time, these problems will diminish or cease to exist. Institutions should be more open to recent technologies, beyond the so-called technophobia that exists in healthcare.
This resistance to any kind of technology (which tend to bring benefits) It is something that will only delay the institution for progress!
The idea of technologies such as AMI systems are mostly proposed to assist doctors and health professionals in their daily activities, in a modern and safe way.