With the advent of electronic medical records and wearable technology, data is becoming exponentially abundant in healthcare. Artificial intelligence has the potential to reshape medicine.
For example, AI can be used to help doctors with clinical decisions, find new types of tumors in large research datasets, improve the accuracy of diagnostic tests, and improve hospital operations. The applications of AI are endless.
Pre-med students can get involved at the intersection of AI and medicine by learning specific skills and working on research projects.
Advancing Healthcare with AI: Innovative Applications
Dr. Justin Norden became interested in medicine when he entered Carleton College in Minnesota, but he also became interested in computer science. What started as a computer science course to learn the basics of programming turned into an intellectual passion, and Norden majored in computer science while meeting his prerequisite requirements.
“My computer science classes gave me a better understanding of programming, data structures, and algorithm design,” says Norden. “All of this has helped me develop machine learning and AI applications in the future.”
Norden wanted to gain a deeper understanding of how his computer science degree could be applied to genomics and other health data applications, and he pursued a Master of Philosophy in Computational Biology at the University of Cambridge. before entering Stanford University School of Medicine.
Norden first applied his AI skills to analyze a large database of RNA sequencing data to understand the risks of developing colon cancer. He used machine learning techniques to create biomarker gene signatures to assess cancer risk.
At Stanford, Norden was curious about how to apply AI to digital health. He wanted to understand if data from wearable technologies could be leveraged to identify movement patterns in individuals and give clinicians a more accurate understanding of a patient’s disease. In a research study, he looked at accelerometer data in 4,000 patients and found several characteristics that could differentiate between normal individuals, individuals with spinal stenosis, and individuals with knee osteoarthritis.
Finally, Norden wanted to see how new technologies affected healthcare delivery, so he joined the Stanford Center for Digital Health. He has evaluated new technologies and studied the impact of digital health solutions. For example, one study assessed whether there were differences between in-person visits and telemedicine visits in how doctors ordered prescriptions, lab tests, procedures, and images.
Develop skills, AI project experiences as Premed
Norden advises premed students to learn important AI skills first.
“I would highly recommend learning computer science. The more technical skills you develop before you start your clinical training,” he says, “the more you will be able to bridge the fields of artificial intelligence, technology, and Medicine “.
Knowledge of math, data analysis and clinical medicine will be helpful, adds Norden. While he was officially pursuing a major in computer science in college and a master’s degree in computational biology, he encourages premeds to learn in informal arenas.
“There are plenty of great free online courses that will teach you the necessary skills online,” says Norden. “Interested predocs have more access to AI learning than ever before.”
Once predocs have acquired some coding skills, Norden strongly recommends a next step: “My biggest advice for predocs is to find a project and a mentor – get involved in an AI project because there’s so much opportunities and a great need for health care for these revolutionary discoveries.”
Here are some leads for pre-med students interested in AI projects:
One of the most common applications of AI in medicine is precision medicine. AI can be used to understand patient attribute patterns and then recommend a treatment plan based on AI analysis.
For example, a physician can examine the specific genotype of a patient with cystic fibrosis to guide a personalized treatment regimen.
Patterns in large clinical datasets
AI is commonly applied to large clinical datasets to help researchers analyze data patterns. For example, AI techniques have been used to understand gene expression data and tumor markers in cancer. In fact, researchers have been able to discover new cancer subtypes using this AI method.
An increasingly popular area among start-ups is the application of AI in radiology to more accurately identify abnormalities in patient diagnostic tests. For example, AI is now able to diagnose diseases on chest X-rays, such as the early stages of pneumonia.
Clinical decision support
Physicians and administrators can help design AI algorithms to guide physicians in clinical decision making. For example, certain patient risk factors can alert physicians that a patient is at risk for infection. Then the doctor can either monitor the patient more closely or prescribe medication to prevent infection.
Hospitals are only beginning to discover how AI can be applied to hospital operations to increase patient follow-up appointments, optimize appointment slots, and improve billing. For example, AI can be used to identify patients who need follow-up appointments and then send them reminders to schedule one.
Another example is that hospitals can develop AI tools to calculate optimal operating room efficiency. A more recent and innovative application of AI in hospitals is emerging through smart sensors, which can detect a patient’s fall and immediately alert healthcare providers.
natural language processing
Speech recognition is a non-medical AI application that gets better over time, and it’s also useful in medicine. Physicians use voice recognition software to dictate their notes, which improves physician mapping efficiency.
Natural language processing also powers medical chatbots that interact with patients to provide immediate answers and a first layer of medical support.
AI is revolutionizing healthcare, and premedics can explore exciting ways to advance medicine and scientific research in this interdisciplinary field.
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