AI and machine learning have created significant value across the healthcare delivery lifecycle.
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Medicine has always functioned as an “evidence-based” field. This means generally pursuing experiments to gather evidence to support specific claims for diagnostic and therapeutic success. For example, drugs often act based on the law of statistical averages. This means that if an overwhelming majority of people respond positively to a particular drug, that drug is generally prescribed more broadly to cover a wider segment of the population until evidence proves otherwise. However, this also means that a portion of the population is susceptible to errors and may experience side effects, lack of treatment efficacy, or even more harmful consequences of inappropriate treatments.
With the advent of advanced machine learning and deep learning models, this precedent is gradually changing, and drug designers and therapeutic manufacturers are increasingly finding ways to move toward predictive, genotype-driven treatments. One important aspect of this pursuit is the research and development of more advanced tools in the field of pharmacogenomics. This is based on understanding an individual’s unique genetic makeup and how they respond to certain drugs. A recent study published in the Annals of Medicine and Surgery discusses how incorporating pharmacogenomics into precision medicine has created significant new opportunities for personalized treatments. Specifically, machine learning models that can synthesize large amounts of genomic and population-level data to determine customized dosing and side-effect trends, as well as AI-powered drug-gene interaction modeling, have made genotype-based treatments more accessible. As the authors point out, these opportunities are already being exploited in some of the most important specialties, including psychiatry, cardiology, oncology, and infectious diseases. In psychiatry, ML models are increasingly capable of predicting antidepressant treatment resistance in advance. This is a common problem and point of frustration for this patient group. In cardiology, genotype-specific dosing of warfarin and clopidogrel has provided significant benefits in reducing mortality and adverse event rates. In oncology, perhaps the most successful field in pharmacogenomics, machine learning models can analyze new granularities of biomarkers, enabling “hypertargeted therapies that attack tumor-specific mutations with astonishing precision.”
This is the future of personalized, personalized medicine.
Another study published in Frontiers of artificial intelligence We explain how medicine’s shift from a one-size-fits-all approach to selective treatments is one of the greatest changes in the history of the field. This is primarily made possible by the ability of frontier models to unlock entirely new ways of analyzing and calculating data. When it comes to disease diagnosis, ML models are creating significant opportunities to improve early detection. “Machine learning algorithms have proven highly effective at recognizing patterns within complex data sets, allowing for early diagnosis of conditions such as cancer, diabetes, and cardiovascular disease, often before clinical symptoms appear. Developed to analyze medical images such as scans with high accuracy, these models use large datasets of labeled images to help radiologists identify potential problems faster than traditional methods by “learning” features that indicate early-stage cancer. ‘s ability to analyze genomic and clinical data will also extend to predictive analytics that predict the likelihood of disease onset and enable preventive interventions by integrating patient medical history, lifestyle data, and genetic information.
Why is all this important?
The frontier AI models that are popular today are incredibly powerful. But more importantly, this is the worst thing ever for them. With billions of dollars being poured into AI research, an incredible appetite for investment in this technology by both the retail industry and the corporate economy, and a rapidly deteriorating overall morbidity index globally, the environment is ripe to leverage the best parts of this technology to truly transform diagnostics and precision care in the coming decades. If developed correctly, AI has huge potential to create tremendous value across the healthcare value chain, from diagnosis and detection to precision drug design, curated therapeutics, remote patient monitoring, post-treatment follow-up, and longevity care, and if developed correctly, there is much to be gained from this technology.
