How Is AI Used in Medicine?
Nearly every industry has implemented some level of AI to help promote efficiency. However, since AI technology is not always accurate or objective, these applications will unlikely replace human workers entirely.
Actual healthcare providers should carefully monitor AI equipment for it to be most effective. Currently, AI in the medical industry has the capability to help:
- Diagnose medical conditions
- Develop prescription medications
- Monitor patients
- Cross-check for drug interactions
- Predict the development of medical conditions
- Detect dislocations, soft tissue problems, and hidden fractures
- Establish a prognosis for severe medical conditions, such as amyotrophic lateral sclerosis (ALS)
- Identify and diagnose certain types of cancers
- Conduct in-depth medical research promptly
- Interpret diagnostic test results, such as chest X-rays
- Analyze existing online medical journals
What Happens When AI Makes Errors?
When artificial intelligence makes mistakes, people can suffer debilitating injuries, illnesses, and even death. While AI can go a long way in improving patient health, there are also substantial practical risks of AI.
First, it is essential to consider how AI algorithms work. They identify patterns and use pattern recognition to flag findings that may be abnormal. However, when AI algorithms misinterpret these patterns, they can misdiagnose medical conditions and cause inaccurate imaging results. If healthcare providers rely on AI technology alone, patients may go untreated, be misdiagnosed, or undergo unnecessary medical interventions, such as pharmaceutical treatment or surgery.
Artificial intelligence in the healthcare industry can also introduce additional problems not typically seen with human healthcare providers. For example, physicians often err on the side of caution when analyzing images, diagnostic test results, and patient symptoms. However, with AI, they may not be programmed to return results in the same way.
Ethical Concerns With AI
Data privacy issues are already a severe cause for concern across multiple industries. However, in the healthcare industry, patient records should always be confidential. Any time healthcare systems share patient data with AI developers, this may violate a patient’s privacy.
AI systems are also developed with the programmer’s inequality and bias in mind. This means specific demographics could go underrepresented in AI data and continue to face distinct challenges as a result.
For example, suppose Black patients are generally prescribed painkillers less often than white patients. An AI system’s algorithm could then lead to a Black patient being prescribed a much lower dose of painkillers than a white individual who experiences less pain.
