Co-founder and CEO of the generation capital organization TenderHut. Entrepreneur and investor. More than 20 years of experience in the IT sector.
Robots in operating rooms have been around for several years, but doctors still follow them. What will happen when they get “smart”?Will they upgrade fitness professionals?Will they perform the most complex procedures without human supervision?
Robots equipped with synthetic intelligence (AI) and capable of analyzing giant amounts of knowledge will be able to perform the most productive procedures taking into account (among other things) the patient’s medical history, the operations performed in thousands of instances similar to the given case and the threat of complications.
Robotic arms are more precise than the human hand, and AI will take on this role. This precision will lead to greater effects in surgical procedures and decrease the number of headaches and shorten recovery time.
However, it is more complicated to exercise a robot than a human being to become a surgeon. AI wants batch knowledge, i. e. all data similar to the progress of the operation from start to finish. If we can already collect, analyze and percentage this knowledge using the combined truth (for example, in the practice of doctors or operations performed remotely through groups from other countries), it is still too little to let the robot “independently” the course of the operation.
Like “intelligence,” “artificial intelligence” also doesn’t have a single definition. In short, a PC formula can mimic the human habit of performing a task. Predicts or takes measurements based on patterns based on collected data. The more mature AI is, the faster and more as it needs to be, it processes increasing amounts of data, so it can be used in virtual assistants, autonomous cars or robotics. Robots usually have a physical form and control software. Robots controlled through AI software move autonomously. ; They don’t want direct orders from a human.
Today, AI systems are used, among other things, for medical diagnoses, research of imaging test results, help in deciding the optimal remedy for patients, and execution of new drugs. The Covid-19 pandemic has accelerated the widespread use of AI to monitor and analyze public health.
Many experts see the prospect of AI in the context of an aging society and an ever-increasing organization of other people suffering from chronic diseases and fitness formula issues, such as shortages and similar difficulties in accessing medical services. We try to make sure that the controlled AI robots carry out the most accurate medical procedures in the future.
The prospect of AI being used in healthcare is huge. Imagine the odds that arise from combining the wisdom of other corporations and organizations to create a unified AI formula aimed at treating people. Such a joint effort can lead to a breakthrough in personalized medicine, early diagnosis and effective remedy of many diseases.
AI algorithms are only as intelligent as the knowledge with which we exercise them. If we provide AI with partial or incomplete knowledge, AI will recommend partial or partial answers to problems. This is especially important in healthcare, where AI is believed to help make decisions about patient treatment. AI with sufficient computing power and correct data will be virtually foolproof, allowing many patients to temporarily regain their health.
We are looking for the most productive tactics to collect and process medical information from which AI can gain insight. The Internet of Things (IoT) can help us with information gathering. Today, when we are sick, we consult a doctor to get a diagnosis. of the disease and check-ups. Coming soon, our wearables and other devices will send all the knowledge to the medical system, from the patient’s temperature and well-being to the schedules and doses of medications taken. tests while bringing fever to our beds.
In their studies and recommendations on AI in healthcare, the World Health Organization (WHO) and the European Parliament have drawn attention to several key issues. This includes the appropriate variety of knowledge and the elimination of errors resulting from differences between the actual clinical environment and the environment in which the AI was built.
Training AI systems on poorly constructed datasets also carries the threat of bias and inequality of AI algorithms based on gender, ethnicity, socioeconomic status, age, or place of residence. After all, the purpose is for AI in medicine to gain advantages. Widest imaginable organization of people, facilitate diagnoses and recommend appropriate therapy.
As far as we can advance in the progression of technology, the ultimate duty of clinical decisions lies with a human being: a physician.
In the near future, we solve some critical problems.
1. Develop effective strategies for the collection and analysis of medical data, taking into account the need for a variety of data appropriate to errors similar to differences in operating environments or discrepancies similar to the origin and social prestige of patients.
2. Solves the challenge of IA. La bias AI can exaggerate irrelevant and other knowledge, which is very important for processing. We want to expand our skills in AI training.
3. Develop tactics to verify the effectiveness of robots with AI and solve liability problems for possible mistakes they make.
4. Teach patients and doctors correctly.
We have a tool in our hands. As with any device, we can do a lot of smart things with AI but also a lot of damage. Their next destination depends on us, the people.
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