The one-size fits all practice has been the standard healthcare approach for years. But, with the ever-increasing influx of diseases, the success rate of this methodology is declining. The generic assessment of diseases and treatment sometimes leads to incorrect diagnoses, ineffective medications, and, rarely, demise. And for now, the broader healthcare fraternity sees this as the only way for treatment and diagnosis.
A recent study found that only 7% of Americans are satisfied with the current medical system, and over the past 30 years, the public perception of healthcare in the region has been low. Seven out of ten Americans feel the healthcare system in the country is in a state of crisis and perhaps needs a complete overhaul. What the industry needs is finding lucrative and effective ways of treatment for higher success rates, a positive perception, and in turn, a boost in reputation.
This is where precision or personalized medicine comes to help. It has slowly gained traction in life sciences and healthcare as a novel and alternative disease prevention and treatment method. Personalized medicine is an approach to healthcare that focuses on providing treatments and therapies tailored to each patient's unique needs. Such an approach increases the chances of curing and preventing disease manifold. But developing precision medicine is a complex task.
The advent of AI is substantially boosting the development of precision medicine. One of the most significant benefits of AI in personalized medicine is its ability to analyze vast amounts of patient data and identify patterns that may not be visible to the human eye.
AI helps access every individual with their genome, essentially helping in gaining insights into their DNA and identifying any diseases or pre-existing conditions from medical history to find out accurate cures.
Every individual is different, and so is their reaction to pathogens. What AI does is remove the guesswork from diagnosis and treatment plans. Personalized medicine takes it further to eliminate the law of averages and precisely detect abnormalities to provide the best patient care.
While one may think about how much it will cost, thankfully, the costs incurred in applying AI to medicines have been reducing as companies move more advanced in using secure AI models and applying better analytics to the data at hand. Courtesy of that, in 2020, there were 286 personalized medicines available in the market as opposed to only 36 in 2010.
Personalized medicine is predicted to be a game-changer in transforming how healthcare is traditionally delivered. It guarantees to deliver on the promise of effective patient care, substantially helping boost a positive perception of healthcare.
However, how does AI exactly help in developing and improving personalized medicine? This blog will answer the questions.
AI leverages the power of Data Analytics and Machine Learning to deliver effective treatment to patients.
Based on genetic makeup and medical history, AI can design new drugs unique to each patient, which are more effective and have lesser side effects. By analyzing and generating vast amounts of data, healthcare providers can identify precise medical treatments for a patient’s specific condition.
AI can train predictive models to identify patients most likely to respond to a particular treatment, optimizing patient outcomes and reducing side effects.
AI can produce synthetic medical images to train computer vision algorithms to detect and diagnose medical conditions. This aids medical providers in recognizing the most viable treatment options based on a patient’s individual characteristics.
Patient data, when combined with AI-generated data and machine learning, helps caregivers identify suitable treatment options to cut healthcare costs in the long run.
AI can develop predictive models using machine learning that analyzes a large amount of data to estimate personalized medicines' efficacy and potential side effects.
AI can simulate drug interactions to predict personalized medicines' side effects, helping caregivers adjust treatment plans that minimize side effects.
AI reduces the risk of adverse drug reactions by analyzing the patient’s genetic makeup to identify how that patient will react to specific medications.
AI can be used to analyze the data from clinical trials and predict the efficacy and side effects of personalized medicines helping healthcare providers make informed decisions.
AI can help to develop new personalized treatment options based on patient data in the following ways –
AI has the ability to analyze patient data, such as medical records, test results, and genetic information. Patterns and correlations are discovered that may not be immediately apparent to human clinicians, helping identify potential treatments and therapies.
By analyzing vast amounts of data and simulating the effect of different drugs, AI can identify potential treatments that traditional methods may have yet to discover.
Once a treatment has been identified, AI can optimize the dosage and timing of the treatment based on the patient's characteristics. By analyzing data on the patient's metabolism, genetics, and other factors, AI can help personalize the treatment plan to ensure the best possible outcome.
AI reduces costs and automates tasks so caregivers can focus on providing high-quality care for patients instead of being stuck in administrative tasks.
Considering the lightning speeds at which AI analyzes patient data, it helps reduce time and costs in reviewing and analyzing this data.
After determining the treatment options, AI can develop a personalized treatment plan for a patient. Optimizing the dosage and timing of the treatment and identifying potential interactions with other drugs are some tasks undertaken by AI. By automating this process, AI can reduce the time and cost of developing a treatment plan.
AI can also automate many tasks in delivering personalized treatments, such as monitoring patient progress and adjusting treatment plans. This can reduce the need for frequent in-person visits to the clinician, which can be expensive and time-consuming for both the patient and the healthcare provider.
Patient data has to be complete and accurate for AI to succeed at developing and improving personalized medicines. Incorrect, poor-quality or biased data may lead to inaccurate results. Further, there is the question of data privacy and security. AI relies on access to large amounts of patient data, which raises concerns about data privacy and security. Stakeholders must ensure that patient data is stored securely with the appropriate safeguards to protect patient privacy.
Unstructured or non-standardized data can make it difficult for AI algorithms to analyze and interpret the data accurately. Also, the use of AI in healthcare and life sciences is subject to regulations and standards, which may vary by region and are complex and time-consuming to understand.
AI can be difficult to interpret, making it challenging for healthcare providers to understand how the model arrived at its recommendations. Human oversight is also mandatory, as even when AI automates multiple tasks in personalized medication, it is essential to check that the results are accurate and appropriate for the patient.
The question of bias comes into the picture as the output AI is based on the data given to it. This can result in discriminatory or unfair outcomes. Hence, the data must be diverse and representative of the overall patient population. Patient consent must be taken to inform them how their data will be used. Healthcare providers must be fully accountable and transparent for the decisions made by AI algorithms. The benefits of personalized medicine must be distributed equally amongst all patients, irrespective of their social-economic status.
AI’s penetration into healthcare has unlocked new patient care and disease prevention paradigms. And it doesn’t stop there. AI is now being used in pretty much all facets of healthcare, promising to bridge gaps between patient expectations and treatment methodologies.
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