CODEX Digest 06.11.26

Discussing diagnostic uncertainty with patients and families is challenging, but clinicians must be transparent about what is not yet known while remaining sensitive to its impact. This commentary from an uncertainty researcher and their child examines the emotional toll of diagnostic uncertainty on patients and families. The authors suggest that understanding patients’ experiences of diagnostic anxiety and illness severity can help tailor information and care, build trust, and support shared decision-making.

AI integration is a promising approach for expanding service access in settings with limited access to specialists. This systematic review from settings examines the use of AI to support clinical decision-making with point-of-care imaging in global health settings, such as colposcopy, retinopathy screening, and DVT diagnosis. The findings suggest that AI may achieve strong diagnostic performance and move practitioners from nonspecialist to specialist-level performance. However, important evidence gaps remain in understanding its true potential, including limited evaluation of patient outcomes, explainability, regulatory readiness, and performance across diverse clinical and geographic settings.

Patients with rare diseases often experience substantial diagnostic delays. This systematic review examines how NLPs and LLMs are being used to analyze unstructured clinical notes, where key diagnostic information is frequently documented. Of 27 studies, a few showed increased genetic testing and identification of a condition; but most studies relied on retrospective, single site studies, and low validity.

Novel innovations in cancer diagnostic care at a population level may have unintended "spillover" side effects. This study compared cancer diagnostic delays across NHS regions participating in the large multicancer Galleri screening trial with those that were not. Participating regions experienced a small but statistically significant increase in diagnostic delays during the trial’s first six months in part possibly due to increased cancer referral patterns, highlighting the importance of evaluating system-level consequences alongside potential benefits of new screening technologies.

Anchoring bias happens when physicians fail to adjust diagnostic decisions despite conflicting information. This Brazilian eye-tracking study of 32 medical residents examined differences in attention when distracting features were placed in different locations in case vignettes. Accuracy of diagnosis did not differ based on attention to the distracting features, but accurate physicians remembered more critical diagnostic information and less distracting features. This implies that the accurate clinicians have different cognitive processing (retaining key data and discarding the distractors) than those who were inaccurate although they viewed the information similarly.

Physical examination is foundational to diagnosis, yet clinical reasoning is not consistently taught as part of the skill. This article describes an innovated approach to medical teaching history which includes a "core" physical exam and "clusters" of exam features that correspond with diagnostic reasoning, with curricular examples and lessons from more than 10 years across several schools.

Federally Qualified Health Centers (FQHCs) are a key source of care for racially and ethnically minoritized populations, yet colorectal cancer screening rates are 30 percentage points lower than in the general US population. In this secondary analysis, a mailed fecal immunochemical test (FIT) outreach program plus navigation to colonoscopy after abnormal results improved screening similarly across racial and ethnic groups compared to usual care. The findings suggest such organized outreach could raise screening rates without widening disparities, an important consideration for FQHCs.

Diagnostic errors threaten hospital patient safety. In this single-center retrospective cohort study, having two or more ambulatory care visits in the 14 days before admission was associated with about twice the odds of harmful diagnostic error in high-risk hospitalized patients, suggesting a useful signal for proactive diagnostic safety surveillance.

Gaslighting is an underexplored form of team dysfunction that can undermine diagnostic safety. This review describes gaslighting in healthcare teams as dismissiveness, undermining, and inducing doubt in colleagues’ clinical judgment. Contributing factors include hierarchy, workplace stress, and poor communication, consequences include emotional distress, loss of trust, and greater risk of diagnostic error. The author argues that organizations should promote psychological safety, open dialogue, and professional respect to mitigate gaslighting in diagnostic teams.

Artificial intelligence may help address workforce pressures in breast cancer screening by supporting mammography interpretation and review. In this retrospective study of 50,000 women from two NHS screening centers, researchers evaluated AI as a replacement for one radiologist in a standard double-reading workflow, including specialist arbitration. AI-assisted screening was noninferior to the traditional approach while reducing overall reading workload, suggesting a potential path to maintaining screening quality with greater efficiency.

Cervical artery dissection (CeAD), a common cause of stroke in young adults, is often misdiagnosed because symptoms are nonspecific. This multicenter international study finds that one in six patients had a possible diagnostic error, especially younger patients, those with migraines, and those presenting with headache and diffuse symptoms.

The use of LLMs in support of clinical practice is being adopted across healthcare, though not as a part of everyday care delivery. This cross-sectional survey finds that 67% of academic hospital medicine faculty across eight institutions use LLMs to support diagnostic reasoning and treatment decisions. However, most are still not using it daily in their practice due to workflow, accuracy, and patient privacy concerns.

Ambient AI documentation is progressively being used to draft clinical notes, but how clinicians edit and revise the records are not well known. This study presents a qualitative analysis of real-world changes to AI-generated care session drafts, highlighting diagnostic, symptom, and lab test refinements made by physicians. The authors identify practical areas for improving and implementing these tools.