Medicine / research / technology
Anesthesia Resident (PGY-3 / CA-2) at Beth Israel Deaconess Medical Center / Harvard Medical School.
As a physician-scientist, Dr. Khan's work focuses on the critical intersections of anesthesiology, chronic pain, artificial intelligence, medical technology, and virtual reality, alongside basic science and translational research.
After medical school, he completed a Postdoctoral Research Fellowship in the Division of Cardiac Surgery at Beth Israel Deaconess Medical Center and Harvard Medical School before beginning his anesthesiology residency.
He is an Anesthesia Resident (PGY-3 / CA-2) at Beth Israel Deaconess Medical Center / Harvard Medical School.
Work across clinical outcomes, cardiovascular science, medical education, imaging, and emerging tools for care.
Full record on Google ScholarEvaluated whether large language models such as ChatGPT can generate and accurately answer anesthesiology board-style multiple choice questions. Found that LLMs show promise as supplementary study and question-generation tools, while identifying key limitations for clinical education.
* Co-first authors
A structured review of the evidence for virtual reality as a training modality for invasive procedures. Synthesized existing literature to argue that VR simulation can meaningfully improve procedural competence before trainees perform skills on real patients.
* Co-first authors
An interventional feasibility study incorporating a VR module into the standard central venous catheter training curriculum for novice trainees. Demonstrated that VR can be practically integrated into residency education and showed measurable improvements in procedural performance.
Developed and validated a low-cost 3D-printed ultrasound probe simulator and cardiac phantom for teaching transthoracic echocardiography to novice learners. Demonstrated that physical simulation using 3D-printed models can provide accessible, hands-on ultrasound training outside the clinical environment.
A comprehensive review of deep learning methods for diagnosing, grading, and managing brain tumors. Surveyed how convolutional neural networks and other deep learning architectures can interpret MRI and histopathology data to assist neuro-oncology decision-making, and identified key barriers to clinical translation.
An invited editorial commenting on the application of machine learning to cardiac surgery decision-making and outcomes prediction. Explores the promise and limitations of algorithmic tools entering the cardiothoracic surgical workflow.
* Co-first authorship is marked alongside author names.
View full publication listAvid app creator building practical digital tools for learners and clinicians.
Anesthesia education for clinicians in training, with specialty learning presented in a clear, focused way.
Built by Adnan A. Khan, MD, as one of his practical digital tools for learners and clinicians.
A practical operating-room communication resource, bringing clarity to the language clinicians use on the job.
Built by Adnan A. Khan, MD, as one of his practical digital tools for learners and clinicians.
Case-based learning across pain procedures and chronic pain, with clinical scenarios that make complex questions easier to explore.
Built by Adnan A. Khan, MD, as one of his practical digital tools for learners and clinicians.
A straightforward way to discover anesthesia research, with papers from the specialty brought into easier reach.
Built by Adnan A. Khan, MD, as one of his practical digital tools for learners and clinicians.
