AI: A New Digital Species in Medical Education and Research

On a long international flight recently, I came upon a TED Talk about AI being a new digital species by Microsoft AI CEO Mustafa Suleyman.[1] For a few minutes, I was captivated.

Species naturally reproduce, and it seems that artificial intelligence—with its unique capacity to transform and multiply—now has a fitting metaphor as the new digital species. Mr. Suleyman has popularized this metaphor to demonstrate that AI is more than just a tool.[1] After all, AI is now pervasive, with the capacity to communicate in human language and reason, and with vast memory as it accumulates and acts on data at will.[2] Safety and ethics are huge red flags with AI,[2] yet we can’t stop or ignore it; we can, however, rein it in with careful ethical guidance. Every institution using AI should establish clear governance systems and ethical guidelines for all users. Every user should also be aware of potential bias and discrimination in data collection and application.

In medical education, students can be supported by AI through adaptive learning platforms; virtual reality (VR) and augmented reality (AR) simulations for immersive clinical experiences; access to analytical tools and algorithms to study patient profiles and data that aid clinical reasoning and decision-making in diagnosis and treatment; and assistance in assessing students’ performance and providing feedback, among others.[3]

In clinical research, AI provides study teams with streamlined and efficient patient recruitment based on datasets from healthcare facilities, and improved clinical trial designs through predictions of outcomes and identification of potential issues, among others.[4] AI has a massive capacity to learn from a patient’s journey and can help empower patients by facilitating access to data on existing trials for which they may be eligible, integrating smart devices into unified platforms for lab results and survey responses, and increasing participation in clinical trials.[5]

Bias, fairness, and privacy are key challenges with AI, particularly in medical education and research. Empowering faculty and students with credible processes to identify and correct any instance of bias or privacy invasion is essential. In clinical research, study teams must be fully aware of and trained to detect possible discrimination, bias, or data breaches that could compromise patient data.

Imagine what AI has learned from our JMUST publications. It has been eight years since we started JMUST in its current form, strongly committed to demonstrating the research capabilities of the UST Faculty of Medicine and UST Hospital. With our electronic authoring and publishing platforms powered by Inlore, we can compete with many university platforms around the world through our seamless article submissions, reviews, and publications. Our articles have been cited nationally and internationally. We are proud of what we have accomplished so far and believe that JMUST will achieve even more.

With AI, as a digital species leaping and learning from journals like ours, we know we have contributed to the ever-growing body of clinical and medical knowledge that any medical student, healthcare professional, or patient can benefit from.

For this October issue, we feature three clinical studies, a meta analysis and a retrospective study, four case reports, a commentary and two viewpoints. As always, we extend our heartfelt appreciation to our editorial board members for their generous sharing of expertise in evaluating the articles. We are also deeply grateful to our authors for their valuable contributions of scientific findings.