AI can apply in many different industries and is often thought of as a way to elevate the way we view technology. In the healthcare industry, AI is expected to improve drug delivery, provide preventive care to patients, remove bias in care, and work with doctors to improve patient outcomes. 2023 will be another year where AI pushes the boundaries of what we consider healthcare to be.
Impact of AI and ML in 2022
In 2022, AI reached accomplishments such as improving clinical outcomes, operational performance, and even health system efficiencies . Healthcare executives felt that AI was beneficial because it automated tasks and allowed large amounts of data to be delivered and processed at fast speeds and low costs . Some tasks that were automated include insurance being authorized before visits, records being maintained with decreased workloads for doctors, and AI providing patient insights when needed . Another important area that AI improved was remote patient technology. In 2022, AI was able to track patient vitals from their homes and notified caretakers of potential hospital visits. This is critical because AI must be able to successfully integrate with different technologies. Therefore, in 2023 we can expect to see more collaborations with AI regarding patient health outcomes.
AI in Drug Delivery
AI is useful for expanding drug discovery in a cost-effective way. For instance, machine learning can help decrease the time needed to create drugs . It does this with machine learning algorithms that create different models to represent various drug formulation designs . This is crucial because it allows researchers to know different materials that can be used and molecules that make drug delivery more effective and targeted. Moreover, machine learning can make connections faster, leading to new discoveries and more effective medicines. Moreover, long-acting injectables allow drugs to be released over a long period of time, which helps patients have fewer adverse effects and not have to worry as much about their medication schedule . However, this requires a lot of testing to figure out what amount of drug is needed. Machine learning helps accelerate this guesswork and allows patients to receive better medicine faster.
AI in Preventive Medicine & Patient Safety
Genetic medicine is becoming more popular due to its ability to provide preventive and precise care. However, predicting accurate models requires large amounts of data to be processed and cleaned in an efficient manner. This is where AI comes in. AI can help identify which patients would be most successful in various clinical studies. This is especially useful for cancer patients who are given treatment that might depend on guesswork. Biomarkers can be used with AI algorithms to predict the long-term success rate for different treatment plans . It can also be used to predict side effects and helps doctors track patient data over time. Moreover, AI can provide doctors with more information about environmental or lifestyle factors that may be affecting health outcomes.
AI will also be used to provide automated drug delivery to patients in real-time using wearables and sensors. When adverse side effects are detected, AI can alert the medication device to deliver medicine to the patient . This can be useful in life-threatening emergencies that require immediate action. AI can also be useful in providing doctors with important information about when patients faced worsening symptoms, and if there were any prior external factors that led up to them. This will allow doctors to have better information on hand when treating and diagnosing patients.
AI in Health Equality
Bias still exists in healthcare and largely targets minorities. This can lead to poor health and even death when patients are not taken seriously due to these biases . Since AI uses databases with biases, it may implement these in its own models. For instance, AI may send out alerts for a new study that may be useful to a certain group with similar health issues. However, AI may not send alerts to a patient of a certain race, gender, or socioeconomic factor. This will result in the patient missing out on newer and more effective treatment options, and can lead to their symptoms worsening . To address this, algorithms need to be changed to reflect how different illnesses show up in different groups. Moreover, AI needs more testing and data to make sure that health biases are not showing up in its predictions.
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