AI will trigger changes in the medical field in these five directions
For centuries, doctors have been fighting sudden diseases such as fractures, injuries, and infections. "If you are unfortunate enough to get an infectious disease, go to the hospital to see a doctor, get a prescription, and then you can go home and raise it," said Balaji Krishnapuram, director of IBM Watson Health and distinguished engineer. Today, medical health concerns are mainly in chronic diseases such as heart disease, diabetes, and asthma. Because the treatment of chronic diseases often takes a long time, and many times to the medical service institutions. But in a modern society that pursues efficiency, “the old form of medical services didn’t work,†Krishnapuram said. “We need to dramatically improve the patient’s medical experience, and we can transfer more treatments from the doctor’s office to the clinic or even the patient’s home. ." Unlike traditional labor-intensive healthcare, emerging healthcare models are knowledge-driven and data-intensive. Therefore, there will be many new medical service models in the future that rely on a new generation of user-friendly, real-time big data analytics artificial intelligence/machine learning tools. In Krishnapuram's view, the future application of artificial intelligence/machine learning tools and technologies will bring about the transformation of humanity in the following five medical fields: Population management: Identify risks, identify whether a patient is at risk, and identify measures that may reduce risk. Nursing Management: Design a personalized care plan for each patient to narrow the gap in care. Patient Self-Management: Supports and enables patient-customized self-administered treatment plans to monitor patient health, adjust drug dosages, and provide incentives for healthy behavioral changes. System Design: Optimize medical processes – from basic treatment processes to health insurance, through careful data analysis, while improving care outcomes and quality while reducing costs. Decision Support: Help doctors and patients select the right drug dose based on the latest test or monitoring data, assist the radiologist in identifying diseases such as tumors, analyze medical literature, and recommend surgical procedures that will produce the best results. The application of artificial intelligence/machine learning strategies in these five medical fields is essential to creating large-scale, cost-effective, personalized, patient-centered medical clinical systems. In this report, O'REILLY goes deep into these areas and interviews with pioneering experts who combine artificial intelligence and medical care. I compiled the opinions of these experts and hope to share with you the frontier applications and concepts of artificial intelligence in the medical field. Enjoy~ Artificial Intelligence / Machine Learning: The Benefits of Million Patients Artificial intelligence and machine learning have great potential in the field of medical and health. In addition to our familiar level of cancer treatment and diagnosis, AI/ML can also be applied to many medical scenarios: fetal monitoring, early detection of sepsis, combined drug risk. Identification and prediction of rehospitalization, etc. “Medical and biological are very complex, and we often need to go through long-term learning and repeated practice to achieve a certain level of professionalism,†said Dr. Russ Altman, head of biomedical informatics at Stanford University. “Learning and discovering knowledge. In terms of ability, computers can reach maturity faster than humans, which is very exciting." Altman believes that machine learning and neural networks are very useful when discovering the laws of large biological databases. Currently, several of the most promising areas of machine learning in medical research include: "omics data" (genomics, gene transcriptomics, proteomics, metabolomics, etc.) Electronic medical record Personal health, etc., monitored in real time through devices such as wearable devices and smartphones. Real-time or near real-time testing and analysis is especially important in self-managed scenarios. For example, it is important for people with diabetes to accurately monitor their blood glucose levels. If you wait for a doctor or nurse to test, it will affect the accuracy of the results and the proper management of the disease. "If the test results show that your blood sugar is high, it may be that you have consumed too much carbohydrate before the test, or did not sleep well the night before yesterday, or it is too stressed, it may be because this week No exercise. These factors can affect your blood sugar concentration,†explains Krishnapuram. If your doctor conducts a test every two months that you have here, as a basis for adjusting your dose, it is difficult to determine the most appropriate dose for your medication and effectively control your condition. The emergence of AI/ML tools can not only quickly and effectively analyze the results and adjust the dosage of drugs, but also remind patients to keep exercise, eat healthy food and ensure adequate sleep. “Some bad habits need to change,†Krishnapuram said. AI/ML can create a variety of communication channels between health care providers and patients, effectively motivating our behavioral changes. Fashion Leggings,Summer Leggings,Leggings For Women,Stretch Yoga Leggings Xinhui Jielide Garment Factory , https://www.ntgfwear.com
