The Future of Healthcare: Bridging the Gap Between Physicians and AI

As artificial intelligence (AI) systems continue to advance, the healthcare industry is poised for a transformative era. AI tools, such as clinical decision support (CDS) algorithms, have the potential to revolutionize clinical practice by aiding physicians in crucial decision-making processes for diagnosis and treatment.

However, a new perspective article published in the New England Journal of Medicine highlights a significant challenge that physicians face – the need for a unique set of skills to effectively interpret and act upon AI predictions. Many healthcare providers currently lack the necessary knowledge and training to harness the full potential of AI tools.

CDS algorithms encompass a range of technologies, from simple risk calculators to sophisticated machine learning and AI-based systems. These algorithms can predict outcomes such as the likelihood of sepsis in patients or the most effective therapy for an individual with heart disease.

Incorporating these technologies into medical practice requires physicians to understand how AI systems think and work. However, many doctors have not received adequate training in probability and risk analysis, key skills for comprehending algorithmic predictions.

According to Dr. Daniel Morgan, a study co-author and professor of Epidemiology & Public Health at the University of Maryland School of Medicine, “Doctors don’t need to be math or computer experts, but they do need to have a baseline understanding of what an algorithm does in terms of probability and risk adjustment, but most have never been trained in those skills.”

Filling the Knowledge Gap

To address this gap, medical education and clinical training must integrate explicit coverage of probabilistic reasoning tailored to CDS algorithms. The authors of the perspective offered several suggestions:

  • Improving Probabilistic Skills: Medical students should receive early education on probability and uncertainty, using visualization techniques to foster intuitive thinking. This training should include interpreting performance measures, such as sensitivity and specificity, to better understand algorithmic performance.
  • Incorporating Algorithmic Output: Physicians should be trained to critically evaluate and utilize CDS predictions in their decision-making. This involves understanding the context in which algorithms operate, recognizing limitations, and considering relevant patient factors that may affect algorithmic predictions.
  • Practice-Based Learning: Medical students and physicians should engage in hands-on learning by applying algorithms to individual patients and exploring how different inputs impact predictions. Additionally, they should develop effective communication skills to discuss CDS-guided decision-making with patients.

Efforts to bridge this knowledge gap extend beyond individual institutions. The recently launched Institute for Health Computing (IHC) at the University of Maryland aims to leverage AI and other computing methods to enhance disease diagnosis, prevention, and treatment. Dr. Katherine Goodman, assistant professor of Epidemiology & Public Health, will join the IHC to lead educational initiatives.

Dean Mark T. Gladwin of the University of Maryland School of Medicine emphasizes that improving physicians’ probabilistic skills can extend beyond the use of CDS algorithms to benefit evidence-based medicine. The integration of vast amounts of data into machine learning systems will shape a personalized approach to healthcare for each patient.

Frequently Asked Questions

What is a clinical decision support algorithm?

A clinical decision support (CDS) algorithm is a tool that aids healthcare providers in making crucial decisions related to diagnosis and treatment. These algorithms use various techniques, such as regression analysis or machine learning, to predict outcomes and provide recommendations.

Why is it important for physicians to learn about AI and algorithms?

As AI technologies become more prevalent in healthcare, physicians need to understand how these tools function in order to effectively incorporate them into their practice. Learning about AI and algorithms helps physicians interpret predictions, make informed decisions, and provide optimal care to their patients.

What are the challenges faced by physicians in using AI tools?

One of the main challenges faced by physicians is the lack of training and knowledge in probability and risk analysis, which are essential for understanding algorithmic predictions. Additionally, navigating complex AI systems and interpreting algorithmic output can be overwhelming without adequate education and support.

How can medical education address the gap in AI knowledge?

Medical education should include explicit coverage of probabilistic reasoning tailored specifically to CDS algorithms. This involves teaching medical students the fundamentals of probability and uncertainty, fostering intuitive thinking, and providing hands-on learning experiences with AI tools. In addition, effective communication training is necessary to help physicians discuss CDS-guided decision-making with their patients.


  • University of Maryland School of MedicineLink
  • New England Journal of MedicineDOI