AI

May 21, 2026: New Podcast Episode Provides an Editor’s Perspective on the Role of AI in Scientific Publishing

Posted on by Sophia Ramirez

In a new episode of the Rethinking Clinical Trials Podcast, Roy Perlis of Harvard Medical School and JAMA+ AI expanded on key takeaways from his recent Grand Rounds presentation, “Keeping a Human in the Loop: Scientific Publishing and AI.”

Listen to the podcast. For alerts about new episodes, subscribe for free on Spotify, Amazon Music, Apple Podcasts, or SoundCloud.

Scientific communities are divided about the appropriate role of artificial intelligence (AI) in manuscript preparation or review. In the podcast, Perlis revealed some ways AI has made things harder for journals (the increased volume of “slop” submissions); ways it’s made things easier (the writing is better); and ways it might be leveraged to address the industry’s longstanding issues.

“I think AI is going to be very helpful to us in helping to streamline aspects of the peer review process,” Perlis said. “One of the big frustrations I hear from authors, one of my big frustrations as an author, is the incredibly long lag time between submitting something, getting reviews back, and turning it around. In my mind, it’s one way that we slow down science unnecessarily.”

The use of AI may save time and energy when it comes to the rote aspects of manuscript review, such as confirming that a research team followed their analytic protocol or met the criteria mandated by their study design.

“The part where I really value human input, and I think that the machines are still not great at, is: Is this innovative? Is it impactful? Is it significant? Is this really, for the average reader of this journal, going to be useful to them or important to them? I think that’s still very hard to automate,” said Perlis.

He identified trust and discovery as other institutional values that will be maintained through human intervention.

Perlis is a professor of psychiatry at Harvard Medical School. He serves as the director of the MGH Center for Quantitative Health and the editor in chief of JAMA+ AI.

Podcast May 20, 2026: Scientific Publishing and AI (Roy Perlis, MD, MSc)

Posted on by Sophia Ramirez

In this episode of the NIH Collaboratory Podcast, Dr. Roy Perlis of Harvard Medical School and JAMA+ AI expands on key takeaways from his recent Grand Rounds presentation, “Keeping a Human in the Loop: Scientific Publishing and AI.”

Transcript coming soon. For alerts about new episodes, subscribe for free on Spotify, Amazon Music, Apple Podcasts, or SoundCloud.

May 4, 2026: New Podcast Episode Considers Impact of New Digital Technologies on Pragmatic Trial Design

Posted on by Sophia Ramirez

In a new episode of the Rethinking Clinical Trials Podcast, Majid Afshar and Mary Ryan Baumann of the University of Wisconsin-Madison expanded on key takeaways from their recent Grand Rounds presentation, “A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence (AI) to Improve Health Practitioner Well-Being.”

Listen to the podcast. For alerts about new episodes, subscribe for free on Spotify, Amazon Music, Apple Podcasts, or SoundCloud.

The need to document patient visits in the electronic health record is a driver of clinician burnout. The research team sought to assess whether the use of an ambient AI scribe could improve clinician well-being by automating the documentation process. In the podcast, Afshar and Ryan Baumann reflected on some of the forces that guided their trial design.

“The technology itself is in silo, right? The speech-to-text and text-to-speech and [large language models] are very good,” said Afshar. “I think the challenge is actually adapting it to the human use case… We spent a lot of time with our human factors engineering implementation scientists to make sure that the training and onboarding and the use of the tool was appropriate for given end users.”

According to Afshar, this predeployment step led to a fidelity rate of over of 70%.

Once the research team began collecting data, the combination of new digital technologies and a pragmatic setting posed its own set of challenges.

“The pure amount of information that’s coming from each participant is astounding,” said Baumann. “It’s not on a scale that you can look at each individual data point and necessarily understand: Is this an outlier? Did something weird happen on this one day? What are the other elements and factors within a health system that’s still operating as it normally does?”

Ultimately, the research team found a 20% reduction in burnout and a trend towards improvement in professional fulfillment. UW Health scaled the project from 66 to over 600 licenses.

Afshar is an associate professor and director of learning health systems in the Departments of Medicine and Biostatistics & Medical Informatics. Baumann is an assistant professor in the Departments of Population Health Sciences and Biostatistics & Medical Informatics.

Podcast April 30, 2026: Ambient AI to Improve Health Practitioner Well-Being (Majid Afshar, MD, MS; Mary Ryan Baumann, PhD)

Posted on by Sophia Ramirez

In this episode of the NIH Collaboratory Podcast, Drs. Majid Afshar and Mary Ryan Baumann discuss the conduct and results of their pragmatic trial.

Transcript coming soon. For alerts about new episodes, subscribe for free on Spotify, Amazon Music, Apple Podcasts, or SoundCloud.

Grand Rounds April 17, 2026: Keeping a Human in the Loop: Scientific Publishing and AI (Roy Perlis, MD, MSc)

Posted on by Sophia Ramirez

Speaker

Roy Perlis, MD, MSc
Editor in Chief, JAMA+ AI
Director, MGH Center for Quantitative Health
Vice Chair for Research, Mass General-Brigham Psychiatry
Professor of Psychiatry, Harvard Medical School

Keywords

Artificial Intelligence; Publishing; Medical Publishing; Peer Review; Trust

Key Points

  • Though medical publishing was in flux before artificial intelligence (AI) became ubiquitous, the contemporary state of the industry is defined by AI-driven trends: an increased volume of submissions, an increased volume of low-quality submissions, and increased complexity of submissions. Academics are concurrently less willing to participate in the peer-review process – paradoxically making peer review into an area ripe for AI intervention.
  • Scientific communities are divided about the appropriate role of AI in manuscript preparation and review. In mid-2023, JAMA Network journals began requiring authors and peer reviewers to answer questions about their use of AI to create or assist with creation or editing of submitted manuscripts, or with preparation of reviews. While author disclosure of AI use is low, those figures likely represent an underestimate.
  • That peer review improves manuscript quality is a fringe benefit; its real value is instilling trust in the contents. The age of AI poses twin dangers: the simulation and politicization of expertise. AI may be capable of handling rote tasks like checking protocols, human subjects approval, and reporting checklists, but it comes with a potential cost in terms of the public’s already-eroded faith in scientific expertise.

Discussion Themes

The most commonly disclosed use of AI in manuscripts is for language and grammar cleanup, particularly by non-native English speakers. On the whole, AI has improved the clarity of international submissions.

Perlis predicted that administrative editorial tasks will be automated but human editors will remain essential for scientific taste: the ability to curate impactful stories and select which of hundreds of AI-generated hypotheses are worth pursuing.

Researchers were encouraged to master the use of AI agents as local analytic partners or writing assistants to automate aspects of their workflow without losing human oversight.

Grand Rounds April 3, 2026: AI for Diabetes Prevention (Nestoras Mathioudakis, MD, MHS)

Posted on by Sophia Ramirez

Speaker

Nestoras Mathioudakis, MD, MHS
Associate Professor of Medicine
Johns Hopkins University School of Medicine

Keywords

Artificial Intelligence; Diabetes; Prevention; Coaching; Automation

Key Points

  • Of the nearly 100 million U.S. adults with prediabetes, approximately 70% will progress to type 2 diabetes in their lifetime. The Diabetes Prevention Program (DPP), a gold-standard program focused on lifestyle interventions, has demonstrated a 58% reduction in diabetes incidence. However, an effort to implement the program nationally fails to reach 99% of eligible individuals.
  • The research team sought to investigate whether a fully automated, AI-based DPP could effectively replicate the outcomes of the human coach-based DPP and potentially bridge this access gap. This was the first trial comparing a fully automated versus human DPP. It adds to a limited evidence base evaluating AI interventions against established standards in medicine.
  • The research team found that the AI-driven DPP delivered without human intervention was non-inferior to the traditional human coach-based DPP. Participants in the AI-driven DPP arm had comparable health outcomes and adequate engagement – though they were less likely to express a preference for their intervention than those in the human coach-based arm. The study team concluded that diabetes prevention remains an implementation challenge, not an efficacy problem.

Discussion Themes

While absolute weight loss was modest when compared to the effect of new medications like GLP-1s, Dr. Dakis argued that lifestyle interventions remain more cost-effective and that future automation efforts may bridge the effectiveness gap.

In the future, large language models could bolster the trust and human connection lacking in fully automated digital interventions.

April 1, 2026: AI for Diabetes Prevention, in This Week’s Rethinking Clinical Trials Grand Rounds

Posted on by Sophia Ramirez

In this Friday’s Rethinking Clinical Trials Grand Rounds, Nestoras Mathioudakis of the Johns Hopkins University School of Medicine will present “AI for Diabetes Prevention.”

The Grand Rounds session will be held on Friday, April 3, 2026, at 1:00 pm eastern.

Mathioudakis is an assistant professor of medicine at the Johns Hopkins University School of Medicine.

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Grand Rounds January 30, 2026: A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being (Majid Afshar, MD, MS; Mary Ryan Baumann, PhD)

Posted on by Sophia Ramirez

Speakers

Majid Afshar, MD, MS
Associate Professor
Director, Learning Health Systems
Departments of Medicine and Biostatistics & Medical Informatics
University of Wisconsin-Madison

Mary Ryan Baumann, PhD
Assistant Professor
Departments of Population Health Sciences and Biostatistics & Medical Informatics
University of Wisconsin-Madison

Keywords

Artificial Intelligence; Burnout; Provider Well-Being; Learning Health System

Key Points

  • Documentation in the Electronic Health Record (EHR) is a driver of clinician burnout; it can reduce their capacity to connect with patients during an appointment and spill over into daily life, i.e. “work outside of work.” The research team sought to assess whether the use of an ambient Artificial Intelligence (AI) scribe could improve clinician well-being by automating the documentation process.
  • The stepped-wedge, individually randomized trial took place over 24 weeks at UW Health. 66 providers across various specialties were randomized to receive access to ambient AI at Week 1, Week 7, or Week 13. The primary endpoint was physician well-being, measured by the Professional Fulfillment subscale and a burnout composite.
  • The research team found a 20% reduction in burnout and a trend towards improvement in professional fulfillment. These effects were sustained over the course of the trial. Use of the technology also decreased “work outside of work” by an average of 30 minutes and “time in notes” by 21 minutes.

Discussion Themes

Qualitative interviews found that patients of the clinicians in the trial appreciated the reduced computer interaction and felt that communication was clearer and more open.

The research team worked with the EHR company to develop a rubric for evaluating the AI-generated notes for accuracy, i.e. the presence of AI hallucinations or falsifications.

Following the trial’s success, UW Health scaled the project from 66 to over 600 licenses. An operational dashboard was developed for real-time monitoring of utilization and documentation metrics to ensure sustained performance and coding compliance.

January 28, 2025: Assessing Whether Ambient Artificial Intelligence Can Improve Health Practitioner Well-Being, in This Week’s Rethinking Clinical Trials Grand Rounds

Posted on by Sophia Ramirez

In this Friday’s Rethinking Clinical Trials Grand Rounds, Majid Afshar and Mary Ryan Baumann of the University of Wisconsin-Madison will present “A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being.”

The Grand Rounds session will be held on Friday, January 30, 2026, at 1:00 pm eastern.

Afshar is an associate professor and director of Learning Health Systems in the Departments of Medicine and Biostatistics & Medical Informatics. Baumann is an assistant professor in the Departments of Population Health Sciences and Biostatistics & Medical Informatics.

Join the online meeting.

Grand Rounds August 1, 2025: Clinical Trial Notifications Triggered by Artificial Intelligence-Detected Cancer Progression (Kenneth L. Kehl, MD, MPH)

Posted on by Sophia Ramirez

Speaker

Kenneth L. Kehl, MD, MPH
Assistant Professor of Medicine and Physician
Dana-Farber Cancer Institute

Keywords

Artificial Intelligence; Cancer; Notification; Enrollment; Patient Identification

Key Points

  • Historically, less than 10% of adults with cancer enroll in clinical trials. At the same time, many trials struggle to reach their accrual goals. One possible contributor is that many trials of novel therapies for cancer have specific molecular criteria.
  • Dana Farber Cancer Institute (DFCI) developed MatchMiner, a computational matching tool, to connect patients to trials. However, identified patients often weren’t at a place in their treatment when information about trials was relevant. The research team was interested in whether they could train an artificial intelligence (AI) model to identify “trial-ready” patients.
  • The team conducted an implementation pilot, providing clinicians and research staff with weekly spreadsheets containing predictions of clinical trial “readiness” as identified by AI. The majority of identified patients were found to be ineligible upon RN review. Of those who were eligible, the majority opted not to move forward with the trial referral. At the end of the 9-month pilot, 6 AI-identified patients had been consented and enrolled in a therapeutic trial.
  • To assess the impact of AI-driven identification of trial-ready patients, the team launched OPTIONS (Optimizing Precision Trials with an artificial Intelligence driven Oncologist Notification System). The primary outcome of the trial was enrollment in any DFCI therapeutic clinical trial.
  • Patients with solid tumors were randomized into either a control group, in which they could be identified by the standard MatchMiner workflow, or 1 of 2 intervention groups. In the intervention arms, treating oncologists for genomically-matched patients with progressive disease and anticipated changes in treatment were contacted via email. In group 3, patients who met the readiness criteria were manually reviewed before the oncologists were contacted.
  • They found that, though the AI models successfully predicted which patients with active or progressive cancer may need treatment changes, sharing the trial information with oncologists did not increase trial enrollment.
  • This intervention addressed 1 barrier to trial participation. Other barriers may include eligibility criteria that goes beyond genomics and recent progression; and factors related to patient or oncologist preference, such as the motivation for participating, the complexity of the trial, and time toxicity.
  • Dr. Kehl concluded with a reminder that while AI can accelerate clinical cancer research by rapidly identifying clinical trial options for patients, impact requires integration. AI must be applied thoughtfully and continuously evaluated, and researchers should be aware of the pitfalls and shortcuts associated with the technology.

Discussion Themes

The DFCI team is currently working on MatchMiner-AI: an open-source tool that they hope will improve the accessibility of clinical trials for all patients by providing a list of relevant clinical trials. They’re running a pilot study focused on incorporating MatchMiner-AI with the historical tool.

It’s easier to train a model than it is to deploy it in a complicated healthcare context. Given that the tool performs as hoped, there are evidently implementation challenges that still need to be worked out.

The study team considered training the model on a more proximal task – i.e., “Predict whether this patient will enroll in a clinical trial.” However, they were concerned that this would introduce biases – a pertinent concern with AI models – based on which patients typically have the opportunity to enroll in clinical trials.

While there may be use cases in which providing the trial information directly to patients would be more efficient, this would need to be done carefully. Information about worsening cancer, for instance, is best contextualized in a conversation with an oncologist.