AI Clin Med

AI in Clinical Medicine, ISSN 2819-7437 online, Open Access

Article copyright, the authors; Journal compilation copyright, AI Clin Med and Elmer Press Inc

Journal website https://aicm.elmerpub.com

Review

Volume 2, July 2026, e29

Artificial Intelligence Brain Fog and Artificial Intelligence Brain Fry in Healthcare and Their Implications for Clinical Reasoning

↓ **Table 1.** Conceptual Distinction Between AI Brain Fog and AI Brain Fry
Domain	AI brain fog	AI brain fry
The proposed distinctions synthesize literature on cognitive load, cognitive offloading, out-of-the-loop performance, alert fatigue and automation bias [11, 15, 17, 20–22]. The observable markers and remediation strategies are proposed conceptual applications for healthcare practice. AI: artificial intelligence.
Core meaning	Reduced independent reasoning or clarity associated with excessive reliance on AI	Cognitive fatigue caused by excessive AI outputs, alerts, monitoring, or verification tasks
Dominant demand pattern	Under-engagement: too little active generation, retrieval, or synthesis	Overload: too much information, interruption, task switching, or oversight
Main mechanism	Cognitive offloading, automation dependency, and reduced effortful reasoning	Cognitive overload, alert fatigue, vigilance demands, and task switching
Typical setting	Healthcare education, diagnostic workup, imaging review, and literature synthesis	Electronic records, decision-support dashboards, ambient documentation, and messaging systems
Observable clinical marker	Clinician cannot explain or reconstruct a plan without returning to the AI output	Clinician skims, ignores, delays, or indiscriminately dismisses AI-generated material
Subjective or workflow cue	The task feels easy because AI has done the thinking, but understanding is shallow	The task feels mentally saturated; attention and review quality decline as outputs accumulate
Co-occurrence	May follow fry when fatigue triggers shallow acceptance and cognitive offloading	May precipitate fog when overload causes default acceptance of AI recommendations
Ethical risk	Deskilling, shallow understanding, premature closure, and diluted agency	Missed signals, alert dismissal, burnout, and diluted accountability
Immediate remediation strategy	Pause AI; restate the problem, differential, disconfirming evidence, and plan unaided	Reduce and prioritize outputs; suppress low-value alerts; protect review time and redistribute monitoring
Preventive strategy	Human-first reasoning, AI-free assessment, spaced retrieval, and reflective case review	Output prioritization, alert tuning, workflow limits, concise interfaces, and protected review time

↓ Table 1. Conceptual Distinction Between AI Brain Fog and AI Brain Fry

Domain

AI brain fog

AI brain fry

The proposed distinctions synthesize literature on cognitive load, cognitive offloading, out-of-the-loop performance, alert fatigue and automation bias [11, 15, 17, 20–22]. The observable markers and remediation strategies are proposed conceptual applications for healthcare practice. AI: artificial intelligence.

Core meaning

Reduced independent reasoning or clarity associated with excessive reliance on AI

Cognitive fatigue caused by excessive AI outputs, alerts, monitoring, or verification tasks

Dominant demand pattern

Under-engagement: too little active generation, retrieval, or synthesis

Overload: too much information, interruption, task switching, or oversight

Main mechanism

Cognitive offloading, automation dependency, and reduced effortful reasoning

Cognitive overload, alert fatigue, vigilance demands, and task switching

Typical setting

Healthcare education, diagnostic workup, imaging review, and literature synthesis

Electronic records, decision-support dashboards, ambient documentation, and messaging systems

Observable clinical marker

Clinician cannot explain or reconstruct a plan without returning to the AI output

Clinician skims, ignores, delays, or indiscriminately dismisses AI-generated material

Subjective or workflow cue

The task feels easy because AI has done the thinking, but understanding is shallow

The task feels mentally saturated; attention and review quality decline as outputs accumulate

Co-occurrence

May follow fry when fatigue triggers shallow acceptance and cognitive offloading

May precipitate fog when overload causes default acceptance of AI recommendations

Ethical risk

Deskilling, shallow understanding, premature closure, and diluted agency

Missed signals, alert dismissal, burnout, and diluted accountability

Immediate remediation strategy

Pause AI; restate the problem, differential, disconfirming evidence, and plan unaided

Reduce and prioritize outputs; suppress low-value alerts; protect review time and redistribute monitoring

Preventive strategy

Human-first reasoning, AI-free assessment, spaced retrieval, and reflective case review

Output prioritization, alert tuning, workflow limits, concise interfaces, and protected review time

↓ **Table 2.** AI-Related Cognitive Risks Across Healthcare Groups and Experience Levels
Group and stage	Likely AI exposure	Main cognitive risk	Protective strategy
These profession- and experience-specific risks are hypothesis-generating rather than estimates of established prevalence. The proposed mechanisms are informed by literature on out-of-the-loop performance, alert fatigue, automation bias across physicians, nurse practitioners and physician assistants, and AI in nursing education [15, 20, 22, 25]. AI: artificial intelligence.
Health professions students and trainees (medicine, nursing, nurse practitioner and physician assistant, pharmacy, allied health)	AI tutors, case simulators, exam tools, and assessment prompts	Learning an answer or template before developing the reasoning or assessment path	Require independent problem representation or assessment before AI consultation; retain AI-free examinations
Early-career diagnosing and prescribing clinicians (junior doctors, novice nurse practitioners and physician assistants)	Draft notes, differential-diagnosis tools, order suggestions, and risk scores	Premature closure, shallow verification, and reduced tolerance of uncertainty	Supervisor-led comparison of human and AI reasoning; explicit dangerous-alternative and disconfirming-evidence checks
Experienced diagnosing and prescribing clinicians (senior physicians, experienced nurse practitioners and physician assistants)	Risk models, diagnostic second opinions, literature synthesis, and complex decision support	Gradual deskilling in rare or complex cases and automation-induced anchoring	Periodic AI-free complex-case review, peer calibration, and audit of cases in which AI changed management
Early-career nurses and allied health clinicians	Assessment prompts, care-plan generation, escalation alerts, and documentation support	Prompt dependence, over-standardized assessments, and alert fatigue	Require unaided assessment summaries, role-specific supervision, and prioritized escalation pathways
Experienced nurses and allied health clinicians	Triage systems, longitudinal summaries, workload allocation, and treatment suggestions	Loss of situational awareness or patient-specific nuance when workflows become template-driven	Co-design alerts and templates; retain narrative handover, exception review, and patient-centered override pathways
Pharmacists and diagnostic professionals	Medication verification, interaction alerts, imaging or laboratory prioritization, and report drafting	High-volume verification burden, false-positive fatigue, and anchoring on AI labels	Risk-tiered queues, independent first reads for selected cases, and audit of false-positive and false-negative consequences
Administrative and operational staff	Scheduling, recall systems, coding, billing, and capacity management	Automation of errors across large patient groups and exception overload	Audit trails, sampling of automated actions, exception review, and clear human escalation pathways

↓ Table 2. AI-Related Cognitive Risks Across Healthcare Groups and Experience Levels

Group and stage

Likely AI exposure

Main cognitive risk

Protective strategy

These profession- and experience-specific risks are hypothesis-generating rather than estimates of established prevalence. The proposed mechanisms are informed by literature on out-of-the-loop performance, alert fatigue, automation bias across physicians, nurse practitioners and physician assistants, and AI in nursing education [15, 20, 22, 25]. AI: artificial intelligence.

Health professions students and trainees (medicine, nursing, nurse practitioner and physician assistant, pharmacy, allied health)

AI tutors, case simulators, exam tools, and assessment prompts

Learning an answer or template before developing the reasoning or assessment path

Require independent problem representation or assessment before AI consultation; retain AI-free examinations

Early-career diagnosing and prescribing clinicians (junior doctors, novice nurse practitioners and physician assistants)

Draft notes, differential-diagnosis tools, order suggestions, and risk scores

Premature closure, shallow verification, and reduced tolerance of uncertainty

Supervisor-led comparison of human and AI reasoning; explicit dangerous-alternative and disconfirming-evidence checks

Experienced diagnosing and prescribing clinicians (senior physicians, experienced nurse practitioners and physician assistants)

Risk models, diagnostic second opinions, literature synthesis, and complex decision support

Gradual deskilling in rare or complex cases and automation-induced anchoring

Periodic AI-free complex-case review, peer calibration, and audit of cases in which AI changed management

Early-career nurses and allied health clinicians

Assessment prompts, care-plan generation, escalation alerts, and documentation support

Prompt dependence, over-standardized assessments, and alert fatigue

Require unaided assessment summaries, role-specific supervision, and prioritized escalation pathways

Experienced nurses and allied health clinicians

Triage systems, longitudinal summaries, workload allocation, and treatment suggestions

Loss of situational awareness or patient-specific nuance when workflows become template-driven

Co-design alerts and templates; retain narrative handover, exception review, and patient-centered override pathways

Pharmacists and diagnostic professionals

Medication verification, interaction alerts, imaging or laboratory prioritization, and report drafting

High-volume verification burden, false-positive fatigue, and anchoring on AI labels

Risk-tiered queues, independent first reads for selected cases, and audit of false-positive and false-negative consequences

Administrative and operational staff

Scheduling, recall systems, coding, billing, and capacity management

Automation of errors across large patient groups and exception overload

Audit trails, sampling of automated actions, exception review, and clear human escalation pathways

↓ **Table 3.** Cognitive Resilience Framework for AI-Assisted Healthcare
Principle	Clinical practice	Health professions education	Organizational safeguard
This framework is proposed by the authors based on the synthesis of literature concerning AI dependency, cognitive offloading, automation bias, alert fatigue, healthcare education and implementation complexity [17, 19–22, 25, 26, 33]. AI: artificial intelligence.
Human-first reasoning	Clinician records an initial assessment before viewing AI advice	Learners submit a problem representation, assessment, or differential before AI use	Interface design preserves the clinician’s first impression and time-stamps AI exposure
AI as second opinion	AI output is compared explicitly with the human plan	Learners identify agreement, disagreement, and uncertainty	Audit cases in which AI changed diagnosis, treatment, escalation, or follow-up
Verification discipline	High-risk outputs undergo source, consistency, medication, dose, and patient-fit checks	Teach hallucination and bias detection using profession-specific cases	Require review for medications, diagnoses, handovers, and follow-up plans; define accountable reviewer
Cognitive load control	Limit low-value alerts, duplicated outputs, and unnecessarily long generated text	Teach concise prompting, output triage, and stopping rules	Monitor alert volume, review time, override patterns, and output length; tune thresholds by role
AI-free competence	Maintain selected sessions and high-risk rehearsals without AI support	Use AI-free objective structured clinical examinations, simulations, and case discussions	Credentialing and continuing competence include unaided reasoning or assessment tasks
Role- and stage-sensitive use	Match AI autonomy and explanation to experience, role, and clinical risk	Increase scaffolding gradually while preserving independent performance	Co-design with physicians, nurses, nurse practitioners, physician assistants, pharmacists, allied health, and administrative users
Reflective accountability	Clinician documents why consequential AI advice was accepted, modified, or rejected	Case reflection includes ethical, cognitive, and patient-centered analysis	Governance committees review AI-related near misses, adverse events, and inequitable effects

↓ Table 3. Cognitive Resilience Framework for AI-Assisted Healthcare

Principle

Clinical practice

Health professions education

Organizational safeguard

This framework is proposed by the authors based on the synthesis of literature concerning AI dependency, cognitive offloading, automation bias, alert fatigue, healthcare education and implementation complexity [17, 19–22, 25, 26, 33]. AI: artificial intelligence.

Human-first reasoning

Clinician records an initial assessment before viewing AI advice

Learners submit a problem representation, assessment, or differential before AI use

Interface design preserves the clinician’s first impression and time-stamps AI exposure

AI as second opinion

AI output is compared explicitly with the human plan

Learners identify agreement, disagreement, and uncertainty

Audit cases in which AI changed diagnosis, treatment, escalation, or follow-up

Verification discipline

High-risk outputs undergo source, consistency, medication, dose, and patient-fit checks

Teach hallucination and bias detection using profession-specific cases

Require review for medications, diagnoses, handovers, and follow-up plans; define accountable reviewer

Cognitive load control

Limit low-value alerts, duplicated outputs, and unnecessarily long generated text

Teach concise prompting, output triage, and stopping rules

Monitor alert volume, review time, override patterns, and output length; tune thresholds by role

AI-free competence

Maintain selected sessions and high-risk rehearsals without AI support

Use AI-free objective structured clinical examinations, simulations, and case discussions

Credentialing and continuing competence include unaided reasoning or assessment tasks

Role- and stage-sensitive use

Match AI autonomy and explanation to experience, role, and clinical risk

Increase scaffolding gradually while preserving independent performance

Co-design with physicians, nurses, nurse practitioners, physician assistants, pharmacists, allied health, and administrative users

Reflective accountability

Clinician documents why consequential AI advice was accepted, modified, or rejected

Case reflection includes ethical, cognitive, and patient-centered analysis

Governance committees review AI-related near misses, adverse events, and inequitable effects

↓ **Table 4.** Proposed Measurable Hypotheses for Prospective Validation
Hypothesis	Suggested design	Primary measures
AI: artificial intelligence.
H1—Ordering effect (brain fog): Viewing AI before independent problem representation will reduce delayed unaided reasoning and explanation quality compared with viewing AI after an initial assessment.	Randomized parallel or crossover study in health professions learners and clinicians; AI-first versus human-first sequence.	Immediate and 2–4-week unaided diagnostic or assessment accuracy; explanation rubric; differential breadth; recall of case rationale.
H2—Output-load effect (brain fry): Verbose, unprioritized outputs will increase workload and clinically important omissions compared with concise, prioritized outputs containing the same facts.	Randomized interface trial using controlled output length, alert count, and prioritization.	NASA Task Load Index [34]; decision time; review omissions; alert dismissal; error detection.
H3—Discriminant validity: A brain-fog measure will relate more strongly to unaided reasoning and retention, whereas a brain-fry measure will relate more strongly to acute workload, skimming, and missed review.	Scale development with cognitive interviews, factor analysis, and external validation across professions.	Factor structure; reliability; correlations with unaided performance, workload, sleep, and burnout; incremental validity.
H4—Co-occurrence interaction: High output load combined with an AI-first workflow will produce more uncritical acceptance of incorrect recommendations than either exposure alone.	Two-by-two factorial experiment: low/high output load × human-first/AI-first sequence.	Acceptance of incorrect AI advice; commission errors; omitted alternatives; interaction effect size.
H5—Remediation effect: Human-first interfaces plus a disconfirming-evidence prompt will reduce erroneous AI acceptance without a clinically unacceptable delay or loss of benefit when AI is correct.	Randomized non-inferiority or superiority trial comparing standard and resilience-oriented interfaces.	Diagnostic or treatment accuracy; time to decision; appropriate reliance; override quality; near misses.
H6—Role and experience moderation: Brain-fog and brain-fry effects will differ by profession, training stage, AI familiarity, and baseline workload.	Multisite, stratified cohort or pragmatic trial including physicians, nurses, nurse practitioners, physician assistants, pharmacists, allied health, and administrative staff.	Profession-by-exposure interactions; role-specific performance; workload; retention; safety events.

↓ Table 4. Proposed Measurable Hypotheses for Prospective Validation

Hypothesis

Suggested design

Primary measures

AI: artificial intelligence.

H1—Ordering effect (brain fog): Viewing AI before independent problem representation will reduce delayed unaided reasoning and explanation quality compared with viewing AI after an initial assessment.

Randomized parallel or crossover study in health professions learners and clinicians; AI-first versus human-first sequence.

Immediate and 2–4-week unaided diagnostic or assessment accuracy; explanation rubric; differential breadth; recall of case rationale.

H2—Output-load effect (brain fry): Verbose, unprioritized outputs will increase workload and clinically important omissions compared with concise, prioritized outputs containing the same facts.

Randomized interface trial using controlled output length, alert count, and prioritization.

NASA Task Load Index [34]; decision time; review omissions; alert dismissal; error detection.

H3—Discriminant validity: A brain-fog measure will relate more strongly to unaided reasoning and retention, whereas a brain-fry measure will relate more strongly to acute workload, skimming, and missed review.

Scale development with cognitive interviews, factor analysis, and external validation across professions.

Factor structure; reliability; correlations with unaided performance, workload, sleep, and burnout; incremental validity.

H4—Co-occurrence interaction: High output load combined with an AI-first workflow will produce more uncritical acceptance of incorrect recommendations than either exposure alone.

Two-by-two factorial experiment: low/high output load × human-first/AI-first sequence.

Acceptance of incorrect AI advice; commission errors; omitted alternatives; interaction effect size.

H5—Remediation effect: Human-first interfaces plus a disconfirming-evidence prompt will reduce erroneous AI acceptance without a clinically unacceptable delay or loss of benefit when AI is correct.

Randomized non-inferiority or superiority trial comparing standard and resilience-oriented interfaces.

Diagnostic or treatment accuracy; time to decision; appropriate reliance; override quality; near misses.

H6—Role and experience moderation: Brain-fog and brain-fry effects will differ by profession, training stage, AI familiarity, and baseline workload.

Multisite, stratified cohort or pragmatic trial including physicians, nurses, nurse practitioners, physician assistants, pharmacists, allied health, and administrative staff.

Profession-by-exposure interactions; role-specific performance; workload; retention; safety events.