The future of standard medical practice might come sooner than expected through advances in artificial intelligence (AI). Data generated in clinics and kept in electronic medical records allows for more applications of artificial intelligence and high-performance data-driven medicine. These will continue to change how doctors and researchers approach clinical problem-solving.
Still, this has yet to be fully integrated into daily medical practice because even though these algorithms can impact medicine and boost medical interventions, many regulatory concerns need to be addressed first.
Applications of AI in Medicine
An example of how artificial intelligence is being applied in medicine is the highly-publicised company, Aequa Analytics, which uses predictive analytics to assist physicians in treating patients. They have focused on ways to detect interactions between drugs, for example.
Artificial intelligence can be applied to a variety of areas in medicine, including:
AI can help physicians predict when patients might become sick and how long they will live if they are likely to develop chronic diseases like diabetes or are at risk of developing certain diseases.
AI can help physicians better diagnose patients and make more timely referrals to specialists when necessary.
AI can help organisations make better decisions about scheduling, workforce planning, and other areas that impact healthcare.
For example, IBM Watson is currently being used to help patients with various chronic diseases, including diabetes, lung diseases, heart disease, and neurological diseases.
Implications of AI in Medicine
Predictive analytics has the potential to impact physicians’ jobs. Artificial intelligence can help predict a patient’s health outcomes and make appropriate healthcare recommendations.
Artificial intelligence can also help streamline drug discovery and approval. Companies can use AI to track how their drugs are being used and make better projections about their future development successes.
AI research could also aid in finding new drug targets and designing new therapies. For example, in the case of rare diseases, artificial intelligence might be able to comb through the data and find new treatment options for diseases that have no current therapies.
Limitations of AI in Medicine
Although AI may help with medical decision-making, there are many barriers that need to be addressed before broad applications of artificial intelligence in medicine can be made. These include:
The FDA and other agencies are concerned about the potential impact of artificial intelligence on physician decision-making. It’s possible, for example, that an AI system could make recommendations that are poorly informed. That could lead to medical errors and increase medical liability for physicians.
There is also concern that AI systems can only be as good as the data they are using. When the data is unreliable, that could harm the quality of the AI’s output.
Finally, there are limitations to the applications of artificial intelligence in medicine. It’s important to note that AI is still in its early stages, and many unanswered questions need to be worked through before AI can be widely applied.
Conclusion: The Future of Artificial Intelligence in Medicine
Artificial intelligence is not yet ready to replace physicians. Instead, it is being used to streamline clinical research, help predict patient outcomes, and assist in patient care. Ultimately, AI is expected to transform how physicians work by reducing the time spent on administrative tasks and freeing up time for them to spend with patients.
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