Artificial intelligence (AI) is quietly rewriting the future of medicine, from genetic diagnosis to personalized treatment, promising hope for millions of patients.
Medicine has undergone an incredible transformation over the past 50 years, from a field largely based on expert opinion to a formal science. The breakthrough was the advent of evidence-based medicine (EBM) in the 1990s, which helps doctors determine the most effective treatments based on an assessment of the best available scientific evidence. This is high-quality research, clinical trials, and scientific literature that are systematically analyzed and evaluated.
At the turn of the 21st century, precision medicine allows hospitals and health care providers to use a patient’s genetic information, environmental information, and clinical data to personalize care. In practice, precision medicine can be applied to cancer treatment (tailored regimens for each patient), pharmacogenomics (personalized drug doses), cardiovascular and diabetes treatment, etc.
However, the potential benefits of precision medicine and evidence-based medicine also come with new challenges. The amount of data is huge, and the complexity is increasing rapidly. So, how will doctors find useful data for a particular patient? What is the most effective way to interpret the data to choose the best treatment? These are the challenges that scientists are trying to solve.
They are collaborating with experts in genetics, medicine, and environmental science to develop computer systems, using artificial intelligence (AI), to help doctors integrate complex patient data to make the best treatment decisions.
The evolution of evidence-based medicine thanks to AI
In the 1970s, clinical decisions were largely based on expert opinion and experience. In the 1990s, the term "evidence-based medicine" (EBM) was coined, describing the integration of research with clinical expertise when making patient care decisions. This is a medical practice that combines the best available scientific evidence, the clinical experience of the physician, and the patient's wishes.
Randomized controlled trials are considered the best source, as they allow researchers to compare treatment effects with minimal bias. However, collecting and synthesizing all the evidence can be a laborious process. As a result, physicians and patients often rely on clinical guidelines developed by third parties, such as the American Medical Association, the National Institutes of Health, and the World Health Organization. These guidelines provide recommendations and standards of care based on a systematic and thorough review of existing research.
Along with EBM, precision medicine has evolved thanks to two advances: the Human Genome Project (completed in 2003) that identified genes associated with disease and drug response; and electronic medical records (popular since 2009) that facilitate large-scale data analysis. Researchers use AI to mine this information, supporting diagnosis and personalized treatment.
The Human Genome Project has enabled scientists to discover genes involved in thousands of rare diseases, understand why people respond differently to the same drug, and identify mutations in tumors that can be targeted with specific treatments.
Electronic medical records allow scientists to conduct large-scale studies of the association between genetic variants and observable traits, which can inform precision medicine. By storing data in an organized digital format, researchers can also use these patient records to train AI models for use in medical practice.
AI is the key to the future.
The ability to analyze medical data today far exceeds the limits of humans thanks to AI. Each patient has hundreds of genetic variants, environmental exposures, and complex clinical data. AI models can process huge amounts of information, such as detecting sepsis in newborns by analyzing dozens of variables, updated continuously in real time.
Researchers are also using AI to develop tools that help doctors analyze all this data, thereby tailoring diagnoses and treatment plans to each individual. For example, many patients have unique genes that influence the effectiveness of certain drugs. Genetic testing can reveal these traits, but screening every patient is not yet feasible due to cost. Instead, AI systems can analyze a patient’s medical history and predict whether genetic testing would be beneficial based on potential drug treatments.
Another example is the diagnosis of rare diseases using AI. Diagnosis is often difficult because many rare diseases have overlapping symptoms. AI tools can test for genetic traits in patients to determine the cause of the disease.
New technologies will allow the measurement of other biomolecular data besides genetics. AI devices that monitor health in real time can also continuously measure heart rate, blood pressure, and other physiological characteristics.
Leading medical facilities such as the Mayo Clinic, the Cleveland Clinic, the MD Anderson Cancer Center, and Memorial Sloan Kettering Cancer Center in the US are all using AI to determine optimal therapy for patients with cancer, cardiovascular disease, and chronic diseases. Technologies such as IBM Watson Health and specialized AI platforms help doctors analyze genetic information and come up with personalized treatment regimens, helping patients achieve the best treatment results.
Other hospitals around the world are also pioneering in this field. For example, Seoul National University Hospital in South Korea uses AI technology to optimize immunotherapy and chemotherapy. NHS England partners with Google DeepMind to predict risk and personalize treatment. Singapore General Hospital applies AI in genetic research. Apollo Hospitals in India also deploys AI to analyze health and manage chronic diseases.
Overall, AI has become an important tool in leading hospitals, helping to improve the quality of care and bring more accurate and effective treatment paths for patients.
(According to The Conversation)