Personalized Medicine: New Era of Precision Healthcare with AI

Pesonalize medicine
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In the face of increasing medical errors, personalized medicine has become a beacon of hope, offering immense promise for patient care.

This approach considers an individual’s unique genetic makeup, lifestyle, and environment to provide tailored treatment.

And now, with the aid of artificial intelligence (AI), personalized medicine is taking on a whole new level of precision. AI can sort out massive amounts of data to identify patterns and predict which treatments will most likely work for each patient.

In this article, we will explore the fascinating world of personalized medicine, discussing its benefits and challenges and highlighting AI’s essential role in this field.

What Is Personalized Medicine?

Personalized or precision medicine combines multi-omics profiles with clinical, imaging, epidemiological, and demographic data to develop effective, personalized diagnostics and treatments. 

The goal is to provide the right therapeutic strategy based on a person’s phenotype and genotype at the right time.

It focuses on the four “Ps”:

  • Predictive,
  • Preventive,
  • Personalized,
  • Participatory.

This approach is particularly helpful in cancer treatment, where doctors can use specific information about a person’s tumor to diagnose the disease, plan treatment, assess the effectiveness of the treatment, and predict the outcome.

While no one agreed-upon definition of personalized medicine exists, it is widely understood to be a medical model that combines molecular profiling, medical imaging, and lifestyle data.

The idea of precision medicine was first suggested in 1997 by Prawase Wasi as part of the future of genomic medicine. Still, it wasn’t until 2009 and 2010 that “precision medicine” was used synonymously with “personalized medicine.”

Former US President Barack Obama announced the Precision Medicine Initiative in 2015, which helped further to promote the idea of personalized and precision medicine. These concepts are all part of the broader idea of patient-centered care, which aims to better respond to patient needs.

Creating and implementing personalized medicine requires cooperation between disciplines, including bioinformatics, epidemiology, genetics, molecular medicine, pharmacology, and statistics.

Video source: YouTube/Leukemia & Lymphoma Society

How Does Personalized Medicine Work?

Each individual possesses a distinctive genetic composition that sets them apart from others. Take, for instance, the risk factors associated with heart and circulatory diseases like age, cholesterol levels, excess weight, and smoking.

Though these factors are common, they can affect individuals differently based on their genetic makeup.

This means some people may develop heart or circulatory conditions even with the same risk factors, while others won’t. Furthermore, the same disease can progress differently in different people, and people can react differently to the same treatments.

In traditional medicine, treatments are prescribed based on the average results of tests on large groups of people. However, with the advent of personalized medicine, healthcare providers are tailoring treatments to individuals based on their unique genetic, clinical, and lifestyle data.

This approach goes beyond just prescribing the best drugs – it also involves improving diagnosis, predicting the probability of someone developing a condition, and stopping it from worsening.

Advanced technologies, such as genetic analysis, allow scientists to identify common factors and causes of variation, leading to new approaches to diagnosing and treating diseases.

For example, radiation treatment planning for cancer uses advanced imaging techniques and computer simulations to tailor radiation therapy to each patient’s specific needs, delivering targeted treatment to the tumor while minimizing exposure to healthy tissue.

The Role of AI in Precision Medicine

The use of AI treatment planning in personalized medicine is a powerful tool due to AI’s analytical capabilities such as prediction, data integration, and reasoning about underlying patterns.

AI can collect information from diverse sources, including people of varying ages, genders, and health conditions. This enables the development of personalized treatment plans tailored to each individual’s specific needs.

When analyzing biomedical data, important techniques include machine learning (ML) and deep learning (DL). These methods have shown promising results in predicting disease risks with greater accuracy.

ML algorithms can assess diverse patient data, such as clinical, genomics, metabolomics, imaging, claims, experimental, nutrition, and lifestyle. In particular, genome sequencing and phenotyping are two areas where ML is making significant advances.

Machine Learning and Cancer

Machine learning allows researchers to analyze data from different “omics” technologies such as genomics, proteomics, metabolomics, and transcriptomics to study complex biological systems and identify disease biomarkers.

With machine learning algorithms, researchers accurately diagnose subtypes of lung cancer, ovarian tissue, and dermoscopic melanoma images using sample digital scans from The Cancer Genome Atlas.

Proteomics data can be challenging to interpret with limited sample sizes, but combining proteomics and genomics datasets led to the discovery of a new targeted drug for hormone receptor-positive breast cancer.

Researchers also used copy number variations with gene expression data in breast cancer patients to develop new treatment strategies.

Machine Learning and Cardiovascular Diseases

Cardiovascular diseases can be diagnosed with AI using machine learning techniques. For example, one method involves using a neural network classifier to detect congestive heart failure in chest radiographs.

Research has also used generative adversarial networks (GAN) to create visual outputs highlighting relevant aberrant features. 

ML has been applied to echocardiography to differentiate prognostic phenotypes, detect and characterize delayed myocardial enhancement, and automatically assess heart segmentation.

AI has also shown promise in predicting specific cardiovascular events, such as in-hospital cardiac arrest and mortality, and assessing the risk of ventricular arrhythmia. 

Another promising application of AI in personalized medicine is the automatic identification of aberrant electrocardiogram (ECG) results, which can be beneficial as the use of wearable devices grows.

Machine Learning and Drug Discovery

Machine learning analyzes genomics datasets to identify disease subtypes, biomarkers, and potential drug targets.

Major pharmaceutical firms such as AstraZeneca, Benevolent, and GlaxoSmithKline (GSK) have developed collaborations with AI drug discovery businesses and acquired entry to genomic datasets to speed up drug discovery processes.

Another area of therapeutic research that uses ML is genome editing, which helps replace or alter a faulty gene in patients. CRISPR is currently the most effective genome editing technology and is trained with ML algorithms to improve its efficiency and accuracy.

Predictive models are also being developed to reduce off-target consequences that may hamper the technology. 

Video source: YouTube/McGovern Institute

What are the Benefits of Personalized Medicine?

Personalized medicine allows for a shift from reactive treatments to preventive measures. Instead of treating symptoms, it focuses on each patient’s unique genetic makeup to:

  • Predict susceptibility to disease,
  • Improve detection,
  • Customize prevention and treatment strategies.

The benefits of personalized medicine include:

Specific Disease Diagnosis

One of the key advantages of personalized medicine is its ability to detect and diagnose diseases in a highly specific manner.

For instance, a specific mutation in the BRAF gene can be targeted with a highly specific drug, significantly improving the prognosis of melanomas for patients who are not viable candidates for traditional treatments.

Customized Drug Dosage

Personalized medicine also enables genomic analysis to reveal genetic variations that can guide drug dosage, maximizing drug benefits while minimizing side effects.

This approach has been successfully applied to various diseases, including cancer, mental illness, and inflammatory bowel disease.

Disease Prevention

Personalized medicine can also be applied in disease prevention by analyzing a patient’s susceptibility to certain illnesses through sequencing technologies. 

For instance, women with BRCA1/2 gene variations have an 80% chance of developing breast cancer and a 60% chance of ovarian cancer. Identifying these genetic variants allows for implementing preventative measures like surgery or chemotherapy treatments.

Reduced Trial-and-Error Prescriptions

With personalized medicine, the need for trial-and-error prescriptions is greatly reduced. But unfortunately, such prescriptions can prove ineffective and quite costly.

However, by analyzing a patient’s genome, doctors can prescribe drugs that are more specific to their needs, thus cutting down on time and expenses required to identify effective medications.

Enhanced Medication Adherence

Personalized medicine can enhance medication adherence by reducing side effects and increasing efficacy.

Patients tend to comply better with their medications when they become aware of their genetic susceptibility to a particular condition, like high cholesterol.

What are the Challenges of Personalized Medicine?

Personalized medicine has its fair share of drawbacks, alongside its numerous benefits. Privacy is a major ethical concern with the increased use of personalized medicine.

For example, some worry that insurance companies may use genetic information unethically, leading to discrimination against those with genetic predispositions.

Ethical Concerns

There are also ethical concerns surrounding incidental findings, such as discovering a life-altering disease without any treatment options.

This knowledge can lead to mental health problems and greatly impact a patient’s life. Incorrect findings are also problematic, resulting in unnecessary healthcare expenses and psychological distress.

Intellectual Property Rights & Regulatory Issues

The legal sector poses additional challenges to the advancement of personalized medicine. Intellectual property rights issues arise as some believe restricting access to personalized medicine advancements will hinder its development.

Furthermore, regulation laws must be revised to accommodate personalized medicine since the current laws are insufficient.

Personalized Medicine: Key Takeaways

Instead of a one-size-fits-all approach, personalized medicine tailors treatments to the unique needs of each individual patient.

While still in its early stages, personalized medicine has already demonstrated remarkable results in improving patient outcomes and decreasing healthcare costs. However, there are still ethical and legal hurdles that may hinder its progress.

Despite these challenges, personalized medicine offers numerous benefits, including the following:

  • Specific disease diagnosis,
  • Customized drug dosage,
  • Disease prevention,
  • Reduced trial-and-error prescriptions,
  • Enhanced medication adherence.

When considering the advantages of this approach, it is clear that personalized medicine could be the future of healthcare.

As technology continues to evolve and our understanding of the human body deepens, we can provide even more effective treatments customized to each patient’s unique needs.

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.