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Artificial intelligence is becoming progressively sophisticated at performing tasks that humans do, with more efficiently, quickly and at a lower cost. From finding new links between genetic codes to making robotic-assisted surgery, AI simplifies the lives of patients, doctors, and hospital administrators. It is reinvigorating the modern healthcare system through machines capable of envisaging, comprehending, learning and acting like humans or better than them in some way.
AI and robotics have vast transformative capabilities for healthcare. Just the same in today's every-day lives, both technologies are increasingly becoming a part of the healthcare system now. Even, many healthcare organizations are leveraging AI-driven computers to make decisions with slight human intervention. But in the future, it is predicted that these programmed systems will make difficult decisions with their own understanding, without any human interventions.
By leveraging AI and robotics, hospitals can reduce misdiagnosing and medical error rates that cause a large number of deaths every year all over the world. Due to misdiagnosing illness and medical error, 10 percent of all deaths in the United States alone were recorded in the year 2015. So, AI can here be constructive in predicting and diagnosing diseases at a faster rate by analyzing patients' medical histories.
Beyond this, AI can also assist clinicians to take a more all-inclusive approach for disease management, and better coordinate care plans. It also aids patients to better manage and comply with their long-term treatment programs.
As emerging technology applications foster healthier behavior in individuals, AI, on the other hand, puts them in control of health and well-being, enabling healthcare professionals to better comprehend the people's day-to-day patterns and needs they care for.
Utilizing pattern recognition to identify patients' developing risk conditions, or seeing deterioration due to lifestyle, environment, genomic, or other factors, AI can also be helpful and take a decisive approach to cure them.
Improving patient care requires the alignment of health data with apt and timely decisions, and predictive analytics can support medical practitioners to make clinical decision-making and actions alongside prioritizing administrative tasks.
Recently, tech giant IBM, for instance, demonstrated Watson Health, dedicated to data-driven health technologies, that made progress in providing clinical decision support for cancer care. Watson Health is also helping healthcare organizations to implement cognitive technology to divulge a huge volume of health data and power diagnosis.
Robots nowadays have proven themselves a significant part of healthcare. Ranging from simple laboratory robots to highly complex surgical robots that can either support a human surgeon or perform operations by themselves, they have the potential to revolutionize lifecare.
By integrating AI with the advancements in humanoid design can enable robots to go even further and have conversations as well as other social interactions with individuals to keep them healthier with aging minds sharp.
Due to continuing inventions and innovations in technology, biopharmaceutical companies now are taking quick notice of the efficiency, accuracy, and knowledge that AI provides. In drug development, one of the biggest AI breakthroughs came in 2007 when a team of researchers tasked a robot named Adam with researching functions of yeast. Afterward, the robot scrubbed billions of data points in public databases to hypothesize about the functions of 19 genes within yeast, predicting nine new and accurate hypotheses.
Since then, many biopharmaceutical companies have been using artificial intelligence to identify and develop new medicines. XTALPI, for instance, leveraged AI and by combining it, the company's ID4 platform predicts the chemical and pharmaceutical properties of small-molecule candidates for drug design and development.
So, by directing the advancements in AI to streamline the drug discovery and drug repurposing processes, it can significantly lessen both the time to market for new drugs and their costs.
