Artificial Intelligence And Health Care: What Premeds Should Know

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With the advent of electronic medical records and wearable technology, healthcare data is becoming more widely available. Artificial intelligence could change medicine.

For example, AI can be used to help doctors make clinical decisions, discover new types of tumors in large research data sets, improve diagnostic accuracy, and improve hospital operations. The potential applications of AI are endless.

Medical students can participate in the intersection of AI and medicine by studying careers and working on research projects.

Advances in AI in healthcare: innovative applications

When Dr. Justin Norden entered Carleton College in Minnesota, he was interested in medicine, but he also wanted computer science. What began as a computer science course to learn the basics of programming turned into an intellectual passion, and Norden fulfilled his requirements by studying computer science.

"My computer science courses helped me better understand programming, data structures, and algorithm design," Norden said. "All of this has helped me develop future machine learning and artificial intelligence applications."

Wanting to gain a deeper understanding of how his computer science degree applies to genomics and other health data applications, Norden earned a master's degree in philosophy from the University of Cambridge before attending medical school at the University of Cambridge. from Stanford.

Norden first used artificial intelligence skills to analyze large databases of RNA sequencing data to understand colon cancer risk factors. Machine learning techniques have been used to generate biomarker genetic signatures for cancer risk assessment.

At Stanford, Norden was interested in how AI could be applied to digital health. He wanted to see if it was possible to use data from wearable devices to identify people's movement patterns and provide doctors with accurate information about a patient's illness. In one study, analyzing accelerometer data from 4,000 patients, found several characteristics that differed between normal people, patients with spinal stenosis, and patients with knee arthritis.

Ultimately, Norden wanted to see how new technology could affect healthcare, so he joined the Stanford Center for Digital Health. He reviewed new technologies and explored the impact of digital health solutions. For example, one study found differences between in-person visits and telemedicine visits in the way doctors write prescriptions, lab tests, procedures, and imaging.

Development skills, experience in AI projects as a prerequisite

Norden advises pre-med students to first learn the skills necessary for AI.

"I highly recommend computer science. The more technical skills you develop before starting clinical training, the more you can integrate AI, technology and medicine," he said.

Norden adds that knowledge of math, data analysis, and clinical medicine are helpful. Although he formally studied computer science in college and earned a master's degree in computational biology, he informally promotes premedical medical research.

"There are many great online courses that can give you the skills you need," says Norden.

Once pre-meds are good at programming, Norden highly recommends the next step: "My number one tip for pre-meds is to find a project and find a mentor to get involved in an AI project because there are so many opportunities and so much demand. El Mundo Health Service offers these advanced programs." Recommendations".

Here are some opportunities for students interested in AI projects:

the right medicine

One of the most widely used AI methods in medicine is precision medicine. Artificial intelligence can be used to understand patterns in patient behavior and recommend a treatment plan based on AI analytics.

For example, a doctor may evaluate the specific genotype of a patient with cystic fibrosis to develop an individualized treatment plan.

Patterns in large clinical data sets

AI is typically applied to large clinical data sets to help researchers explore patterns in the data. For example, AI techniques have been used to understand gene expression and tumor marker data in cancer. In fact, researchers have been able to discover new cancer subtypes using this AI technique.

Test

One area that is gaining popularity among startups is the application of artificial intelligence in radiology to accurately detect abnormalities in patient diagnostic tests. For example, the AI ​​can now diagnose illnesses such as the early stages of pneumonia on a chest X-ray.

Clinical Decision Support

Physicians and administrators help develop AI algorithms that help clinicians make clinical decisions. For example, some patient risk factors may alert clinicians that the patient is at risk of infection. The doctor may then closely monitor the patient or prescribe medications to prevent infection.

hospital management

Hospitals are beginning to understand how AI can be used in hospital operations to increase patient readmissions, optimize appointment times and improve billing. For example, AI can be used to identify patients who need follow-up and send appointment reminders.

As another example, hospitals can develop AI tools to calculate the optimal efficiency of operating rooms. Innovative applications are emerging in hospitals with smart sensors that detect a patient's fall and immediately alert medical staff.

natural language processing

Voice recognition is one of the non-medical uses of artificial intelligence that has improved over time and is also useful in medicine. Physicians use voice recognition software to dictate their notes, which improves the efficiency of medical records.

Natural language processing enables medical chatbots that interact with patients to provide rapid response and primary medical support.

Artificial intelligence has revolutionized healthcare, and pre-medical institutions can find exciting ways to advance treatment and research in this interdisciplinary field.

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