Artificial intelligence (AI) and machine learning algorithms are causing disruptions in diverse industries, including the medical field. They are changing how things are done while promising faster, better outcomes than ordinarily possible.
Artificial Intelligence
These somewhat controversial technologies are fast evolving into valuable solutions for use in clinical practices. We describe in this article what AI could do in improving medicine as well as the concerns that come with it.
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AI in Medicine
Table of Contents
Artificial intelligence in medicine entails the use of AI and machine learning algorithms to enhance patient experience and health outcomes. These technologies imitate human cognition and help to make approximate deductions relying on inputted data. Their use is gaining ground in healthcare, with medical professionals and researchers turning to them for aid in their work.
AI and machine learning algorithms are designed to carry out tasks that typically require human intelligence to execute. These include pattern recognition, analysis and comprehension of complex healthcare and medical data, and decision-making.
However, these algorithms must undergo training to be able to carry out their tasks. Human experts have to first instruct computer systems on what they must look for in the data or images they are presented with.
AI and machine learning algorithms are trained with large amounts of structured data having recognizable labels or annotations. This is how they become able to provide useful insights and predict with impressive precision.
After feeding them with enough data, programmers assess the efficiency of these algorithms in carrying out analyses and making predictions. They do this testing by inputting test data that they already know the correct answers to. The results of tests are then used to fine-tune these technologies as needed.
Essentially, AI algorithms do only what they have been trained to do by humans. There seems to be no shortage of data to train them with, especially in the West or developed nations where many people have detailed medical records.
The government of the United States is reportedly investing billions of dollars to support the introduction of AI in healthcare.
How Can AI be Applied in Medicine and Healthcare
There are multiple ways in which artificial intelligence is being explored in different areas of medicine. We briefly discuss a few major ones here.
Disease diagnosis
This is probably the biggest use of AI in medicine. Over time, multiple techniques have been employed for disease detection, including neural networks and vector machines.
AI can be used to monitor the vital signs of a patient in critical care and notify doctors if other risk factors emerge or increase. It may be argued, however, that there are already devices for keeping an eye on a patient’s vital signs. AI can try to find signs of more complex conditions – for example, sepsis – in addition.
Personalized medicine
The treatment of diseases could become easier with the use of AI and machine learning models. This is especially vital at a time when more people are embracing telemedicine.
AI can enable remote tracking of patients’ conditions through the use of sensors to aid care. With these technologies, doctors could make personalized medicine recommendations to patients unhindered in real-time. Virtual AI assistants could be used to provide answers to patients’ questions and possibly make suggestions based on their medical history.
Uncovering drug interactions
AI algorithms have been developed to make out in medical literature what is called drug-drug interactions. These pose a notable threat to people who take more than one medicine at the same time. The detection of these interactions (both known and suspected) is hard, but AI algorithms are now helping to make this easier.
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Drug development
The process of making new drugs could become more rapid with the use of AI. The models may shorten drug discovery, a part of drug development that takes a lot of both time and money. They can aid in coming up with improved drug designs and identifying drug combinations having high potential.
Benefits of AI in Medicine
There are multiple benefits to the introduction of AI in medicine and healthcare, including the following:
Reduced misdiagnosis – AI can reduce cases of wrong diagnosis and greatly improve disease detection. There are instances where they outperform human experts. For example, an algorithm known as DLAD did better than 17 out of 18 doctors in the detection of aberrant cell growth from chest radiographs. Researchers also reported in 2020 that an AI system anchored on Google’s DeepMind algorithm outperformed human experts in the detection of breast cancer.
Better patient care – Doctors can get help from AI technologies in making more informed decisions for their patient’s health. They provide valuable insights and add context, which can make a whole lot of difference.
Improved doctor-patient engagement – With the availability of virtual AI support, patients could always get answers and help around the clock. This may aid in ensuring that engagement does not drop when the doctor isn’t available for any reason.
Lower healthcare costs – By reducing the time and costs of drug development, AI may help in lowering the costs of care. Costs could also reduce through the provision of personalized virtual assistance and fewer diagnosis and medication errors, among others.
Concerns and Limitations
It is not all smooth sailing for the adoption of AI in medicine and healthcare, however. Some ethical concerns have been raised and limitations identified.
For instance, there is a concern over patient privacy. Large amounts are needed to train AI. Evidence suggests that most patients are not comfortable with having their information used for this purpose.
The U.S. Food and Drug Administration (FDA) requires complete transparency of techniques used in clinical trials. However, AI algorithms are complex and often involve elaborate mathematics that is hard to work out. Programmers may also be hesitant to reveal their “secret.”
These concerns and limitations, including fear of job loss due to automation, would need to be addressed before large-scale adoption of AI in medicine could become reality. It is unclear yet when algorithms will be able to work fully alone in clinical settings. For now, they could be used to aid clinicians in making more accurate diagnoses and better care decisions.
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References
https://www.ibm.com/topics/artificial-intelligence-medicine
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