Evidence map›Paper›PMID 42857240›Full record

ArticleJournal of CME2026

Practice Makes Human: AI Conversation and Simulation as Deliberate Practice for Human Connection in CPD.

Michael Burke, Jeffrey L Drezner

Abstract read
In one paragraph

Article in Journal of CME, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michael BurkeCopient. Ai, USA.
Jeffrey L DreznerCopient. Ai, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Future-ready continuing professional development (CPD) must integrate artificial intelligence without eroding the human relationships, empathy, and teamwork that underpin quality care. This brief report describes an immersive interactive AI-driven conversational simulation model that supports non-scripted encounters with lifelike avatars, followed by structured scoring and feedback, and repeated practice opportunities to rehearse difficult conversations with both colleagues and patients before they occur. Grounded in deliberate-practice theory, the approach is examined through the five Key Themes central to future-ready CPD as specified for the Journal of CME Special Collection: lifelong learning and wellbeing; quality-driven leadership; equitable and sustainable community health; responsible innovation and digital empowerment; and global equity through collaborative CPD. The report considers adaptive expert-tutor and patient-avatar interactions, individualised Socratic questioning, formative assessment of observable communication behaviours, multilingual and locally configurable scenarios, and auditable governance, including the absence of a formal learner utilisation survey and the early state of evidence linking simulation to patient outcomes. It reviews emerging literature and defines important boundaries: simulated experience is not the entirety of clinical experience; observable empathic behaviour is measurable while empathy itself is more complex; AI feedback can enable deliberate practice but is not infallible; and scalability does not create universality. AI-driven rehearsal is best understood as a scalable complement, not a substitute for traditional clinical supervision, mentorship, standardised patient training, and authentic patient care.

Indexed as

AI-driven avatar simulationcommunications skills trainingContinuing professional developmentconversational AIdeliberate practicehuman connection in patient caremedical education

Identifiers

PMID42857240
PMCPMC13651830

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.