AI in Precision Medicine Market: Growth, Size, Share, and Trends

OPPORTUNITY: The Role of Predictive Analytics in Advancing AI for Healthcare

The transition toward precision medicine is greatly supported because of the possibility of utilizing patient data in real time to customize interventions even before the disease begins to manifest. Predictive analytics involves the application of advanced computational technology such as artificial intelligence and machine learning to collect, collate, and synthesize vast amounts of healthcare data such as genomic data, electronic health records, and medical images together with their associated environmental and behavioral data among others with the sole purpose of predicting risks of illness, outcomes of treatment and other clinical findings and processes. Such a possibility enables the healthcare system to transform from offering only curative services to providing preventive services, which have better patient outcomes and economical utilization of resources.

Machine-learning enabled algorithms to have been created to track the ones likely to be diagnosed with co-morbidities such as diabetes and heart-related diseases in the years coming from their genetic background, lifestyles, and past medical records. This helps employ the ‘treat early’ theory based on simple lifestyle changes or mild preventive medications before the illness shows any sign, thereby avoiding further complications, expensive treatment, or prolonged management. More so, artificial intelligence techniques have made it easier to predict the likelihood of specific cancer treatments, including immunotherapy, being effective in patients based on their biomarkers and the genetics of their tumors.

Predictive analytics supports the drug development process by determining the specific patient subgroups who would respond positively to the investigational therapies, thus maximizing the order and success of the clinical development plan. Tempus and Flatiron Health are two compelling examples of this. They use predictive analytics to perform real-world data analysis as a means of bioinformatics to combine genomic and clinical data for better treatment processes. In addition, predictive analytics determines the number of patients expected to be admitted into the hospital and other resources needed for comprehensive operational planning in the hospital systems.

With the bar of predictive analytics rising, AI in precision medicine can aid in providing efficient, inexpensive, and focused care, which resolves the pain points in healthcare delivery today.

CHALLENGES: The Impact of Fairness and Bias on AI in Healthcare

Fairness and bias are two issues that significantly hinder the application of AI in precision medicine. This is because biased algorithms will result in unfair treatment in healthcare, especially for underserved groups. Algorithms used in precision medicine are primarily developed with unbalanced data sets that discriminate against other population groups. Most genomic data sets are European ancestry dominated, leaving people from Africa, Asia, and Latin America with fewer, if any, data at all. Such disparity creates an enabling environment for the development of AI models for disease risks or treatment interventions of non-European populations, which aggravates the inequality in the health of people in different geographical regions.

As an example, bias is found in genetic risk scores, which estimate an individual’s risk for diabetes or heart disease. Such risk scores are known to give a lower estimate of individuals of African ancestry due to the heterogeneity of the database employed to come up with the score, which was primarily composed of European ancestry individuals. Likewise, cancer diagnosis systems using artificial intelligence that are trained to analyze images of classifications of demographic groups whose majority Caucasian may not be able to detect melanoma or breast cancer in Caucasian patients with darker skin tones, leading to advanced stages of the illness at diagnosis.

Disparities in certain aspects, such as education and the ability to access healthcare among those involved in producing the data, can also cause bias in AI models. This may be where high-income countries with greater access to health care services and the health records of people collected may affect the AI model predictions, whereas regions with lower sick care provision and limited data access suffer. Solutions to these obstacles include more heterogeneous data acquisition strategies, the development of models that incorporate population pyramids, and validation studies. In the absence of these steps, bias in artificial intelligence may thwart the fundamental objective of precision medicine: delivering fair, personalized care to every person.