How Artificial Intelligence is Changing Health care
Machine learning is being used to help doctors understand patients’ outcomes. IBM’s Watson Artificial Intelligence (AI) has the ability to make a diagnosis, apps can track and monitor patient emergencies, our phones may soon be our closest medical advisors; preventative and diagnostic medicine is on the cusp of an artificial intelligence revolution that will no doubt save lives and improve quality of human life.
While there is a great deal of promise in these new technologies, there is tension between innovation and regulation. When it comes to our health, should decisions be made by machines? What role should artificial intelligence play in healthcare?
There is a passionate community of professionals that have a long term vision of Artificial Intelligence in Medicine (AIM). The AIM community has long held the belief that computation, knowledge of presentation, automated diagnosis and planning technologies will have rich offerings for the healthcare field. The ability to capture and analyze data as well as provide access to the best medical expertise on the planet and render it for individual physicians in particular cases makes AI very promising. These technologies can be utilized in a variety of health care-related applications.
One of the most promising areas related to artificial intelligence in the medical field is the reduction of medical errors. Medical error is the third highest cause of death in the U.S., claiming 440,000 lives every year. New technologies, such as health care expert systems, may be able to address these medical errors and save lives through the advancement of data, analytical tools and machine learning.
The AIM community strongly advocates for mining electronic record data, such as diagnostic codes, physician’s notes, lab test results, procedures performed, location of beds occupied in the hospital, billing codes, health treatments given, prescriptions and every medical professional that has come in contact with the patient, to analyze and improve health outcomes for patients. In addition to database analysis, AI has become very precise at predicting which patients will be re-admitted within 30 days based on all of these variables, enabling healthcare professionals to take preventative precautions.
Other ways that machine learning is impacting medicine include the ability to:
- detect, and predict, adverse reactions to medication and devastating diseases, such as lung or liver cancer, by studying time-based patterns of aggregate search engine data
- detect and predict unreported post-partum depression by studying social media data
- use biomarkers to determine which medications might be most effective for certain individuals
- improve diagnostic rates,
- detect and predict cancer and other diseases using comparative biomarkers
- create personalized treatment plans based on specific individual data
- use agentic robots to extend the reach of doctors, nurses and other medical personnel, such has helping aging individuals remain in their home longer through robotic medical monitoring and diagnosis
- use real-time technologies for specialized experts to interact with medical generalists, enabling access to the most current research and treatment options.
While it’s difficult to debate the desirability of the use of AI for the prediction and early detection of disease, insurance companies have been resistant to approving this type of genetic testing because there is a concern about the costs of such tests. The U.S. spends $3.2 trillion on health care every year; roughly 50 percent of that is spent on technology. Who will pay for emerging technologies and will the investment be worthwhile? In addition, there is growing concern about the security of data, who has access to our healthcare data and what might they use it for? What will happen to all of the data that is – and will – be collected and analyzed; will this impact our lives in ways we cannot currently predict?
There are also unanswered questions about liability. Who is to blame when a robot makes a mistake? Currently, there have been no substantial changes to the law; the lack of foresight could result in nobody at all being liable when a negligent incident occurs. Clearly, there are massive potential benefits for AI in health care. On the other hand, there needs to be ongoing efforts to create, update and enforce best practices and standards that will maximize public welfare and safety without stifling innovation or creating unnecessary regulatory burdens.
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