The Pulse
Three things shaping AI in healthcare this fortnight:
Physicians still concerned about AI accuracy amid rapid adoption: survey — While 94% of surveyed physicians report adopting or exploring AI and more than half already use it in practice, over 70% still cite accuracy concerns and nearly half point to legal uncertainty as key barriers to trust. (Healthcare Dive, 2026)
2,400 Kaiser mental health professionals strike in Northern California over AI concerns — A one-day strike involving 2,400 mental health clinicians and 23,000 nurses affecting care for 4.6 million patients, reflecting growing concern that AI could impact clinical roles despite assurances it will remain a support tool. (AP, 2026)
Ethical AI In Healthcare: Drawing The Line Between Innovation And Trust — As AI expands across clinical and patient-facing settings, the need for transparency and clear human decision-making becomes central to maintaining patient trust and clinician accountability. (Forbes, 2026)
Takeaway: AI adoption is accelerating, but concerns about accuracy, trust, and clinical responsibility are shaping how it is used in practice.
Psychology & Behavioral Health
Parents think they know how kids use AI. They don't (BBC, 2026)
Pew data shows 64% of teens use AI, yet only 51% of parents think their child does, and 4 in 10 parents have never discussed it. While most use centers on schoolwork and entertainment, 16% for conversation, 12% of teens report using AI for advice and emotional support, which still represents millions of users. There are also notable differences across groups, with 21% of Black teens reporting AI use for emotional support compared to 13% of Hispanic teens and 8% of White teens.
Clinician Cue: Emotional use may be less common, but it is still meaningful at scale. Ask about AI use directly and assess how it may influence coping, identity, and decision-making.
Classic Psychological Experiment In 1980 On ‘Invisible Scars’ Is A Perfect Explanation For How People Today React To Modern AI (Forbes, 2026)
The “scar experiment” showed that people interpret interactions based on what they believe is true, even when it is not. Applied to AI, this means users who believe AI is highly accurate may over-trust its outputs, while others may dismiss useful information. With millions of people now using AI tools continuously, these perception biases can shape outcomes as much as the technology itself.
Clinician Cue: Pay attention to how patients frame AI. Address over-trust and under-trust directly to support more grounded and critical use.
Medicine & Clinical Innovation
Artificial intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study (BMJ Digital Health, 2026)
In a study of 30 clinicians reviewing 150 CT scans, AI support increased detection of critical abnormalities from 82.8% to 89.7% and improved hemorrhage detection from 84.6% to 91.6%. At the same time, specificity dropped from 84.5% to 78.9%, meaning more false positives were introduced. AI also helped emergency clinicians reach performance levels closer to radiologists, showing its potential to support less specialized readers.
Quick Win: AI can improve detection rates, but expect more false alarms. Build in time to verify findings rather than relying on AI output alone.
Diadia Health Exits Beta as First AI Causal Reasoning Platform for Precision Medicine in Complex Endocrine and Chronic Disease Cases (Health Care Dive, 2026)
This platform analyzes nearly one million genetic variants, over 100 metabolic pathways, and hundreds of biomarkers to generate individualized reports. It draws from more than 310,000 peer-reviewed papers and reports that 98% of outputs require no clinician revision, with claims of reducing trial-and-error treatment by 60%. The focus on causal reasoning and transparency reflects a shift toward more explainable AI in complex care.
Quick Win: When evaluating AI tools, look for systems that integrate multiple data sources and clearly show how conclusions are formed, especially in complex or unclear cases.
Ethics & Oversight
Policy & Compliance: Rapid AI adoption is outpacing clear regulatory guidance, with nearly half of clinicians citing legal uncertainty as a barrier to use.
Bias & Transparency: Patient and clinician perceptions of AI can shape outcomes, making it critical to clearly communicate what AI is doing and where its limits are.
Accountability & Governance: Keep clinical decision-making anchored in human oversight, especially as AI tools expand into diagnosis support, documentation, and patient interaction.
Wayde AI Insight
AI adoption in healthcare is growing, but clinicians remain hesitant about the contexts in which these systems should operate, when to rely on them, and how dependable they truly are. Ambiguity is a recurring theme, as research with teens shows a gap between how AI is actually used and how it is perceived by parents, highlighting how expectations shape engagement. To move forward, clinicians need a careful, unbiased view of AI’s real benefits and limits. Statistical gains are emerging, but the key factor in practice is how humans integrate these tools: maintaining trust, ensuring reliability, and supporting the therapeutic relationship without replacing or diluting it.
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Produced by Wayde AI with AI assistance.