Neuropsychologists

19-3039.02
Median wage $110,840/yr18,820 employed (US)Rank #811 of 923 scored · top 88% by substitution

Apply theories and principles of neuropsychology to evaluate and diagnose disorders of higher cerebral functioning, often in research and medical settings. Study the human brain and the effect of physiological states on human cognition and behavior. May formulate and administer programs of treatment.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution14
Exposure15
Augmentation59

Substitution — the headline: capability discounted by cost, barriers and adoption.

Exposure — technical capability alone, regardless of whether anyone deploys it.

Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.

Tasks on the substitution scale

14 rated tasks, binned by substitution score.

Position among all scored occupations

Distribution of 923 occupation scores; the marker is this occupation.

Tasks with substitution ≥ 70

0%

Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.

Why this score

The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.

Task automatabilityw 35%13

panel mean rating 1.5/5 → substitution pressure 13/100

Technical feasibility todayw 20%18

panel mean rating 1.7/5 → substitution pressure 18/100

Cost vs. human wagew 15%15

panel mean rating 1.6/5 → substitution pressure 15/100

Adoption barriersw 20%inverted — strong barriers lower the score11

panel mean rating 4.6/5 (barrier strength) → substitution pressure 11/100

Sector adoption velocityw 10%18

panel mean rating 1.7/5 → substitution pressure 18/100

Task breakdown (14 tasks)

Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.

Read current literature, talk with colleagues, and participate in professional organizations or conferences to keep abreast of developments in neuropsychology.

44

CI 30–59 · exposure 42 · augmentation 88 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Neuropsychology is a relatively small, specialized, and conservatively-oriented profession with strong emphasis on human expertise and credentialing. Adoption of AI for professional development tasks lags significantly behind faster-moving sectors like finance or tech; practices remain largely manual.
Sector adoption velocityclaude-sonnet-53/5Healthcare and professional/clinical fields show middling AI adoption for literature review and knowledge management, with pilots and tools like AI search assistants becoming more common but not yet deeply embedded in daily practice.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can meaningfully assist by rapidly summarizing new literature, flagging relevant studies, recommending conference sessions, and synthesizing findings—substantially raising the neuropsychologist's efficiency in staying informed while the human retains judgment and collegial engagement.
Augmentation potentialclaude-sonnet-55/5AI literature summarization, alerting, and search tools substantially boost a neuropsychologist's ability to stay current, letting them cover more ground in less time while still engaging with colleagues and conferences themselves.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can summarize literature and identify key developments in neuropsychology, the task inherently requires human judgment to evaluate significance, contextualize findings within one's practice, and engage in collegial dialogue. Reading and filtering alone are insufficient; the qualitative assessment and interpersonal elements cannot be fully automated.
Task automatabilityclaude-sonnet-53/5AI can summarize literature, surface relevant papers, and synthesize updates, saving significant time on the reading portion, but networking, discussion with colleagues, and conference participation require human presence and judgment that AI cannot replace end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Professional development and staying current are often implicit licensing and continuing-education requirements in neuropsychology. Peer interaction and conference participation are deeply embedded in professional norms and credentialing; organizations expect humans to engage directly, creating organizational and professional friction against full automation.
Adoption barriersclaude-sonnet-52/5There's no licensing requirement to read literature or attend conferences, but professional norms, CE/CME requirements in some jurisdictions, and value of human networking create mild friction against pure automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Literature-scanning AI costs are low, but the task includes conference attendance and colleague engagement, which require human time regardless. The hybrid nature means total cost is driven by irreplaceable human components, making AI cost-effective only for the narrowest literature-review portion.
Cost vs. human wageclaude-sonnet-54/5AI-based literature review and summarization tools are dramatically cheaper than a neuropsychologist's billable time spent reading, though the conference/networking portion still incurs full human cost.
Technical feasibility todayclaude-haiku-4-5-202510013/5AI systems can scan and summarize academic literature effectively, and tools exist to curate journal articles and conference abstracts. However, no deployed product reliably captures the nuance of 'keeping abreast' at the depth a neuropsychologist needs, and human participation in conferences and peer discussion remains largely unautomated.
Technical feasibility todayclaude-sonnet-53/5Products like AI research assistants, literature summarizers, and alerting tools are deployed and used by professionals today, but they still have material error rates in domain-specific synthesis and don't cover the social/professional networking components of this task.

Write or prepare detailed clinical neuropsychological reports, using data from psychological or neuropsychological tests, self-report measures, rating scales, direct observations, or interviews.

21

CI 18–25 · exposure 25 · augmentation 63 · importance 4.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Healthcare and neuropsychology are slow-adopting sectors with high regulatory friction, liability concerns, and strong professional gatekeeping. Current adoption of AI for clinical report writing is minimal and heavily supervised; full automation is not occurring in practice.
Sector adoption velocityclaude-sonnet-52/5Healthcare, especially specialized clinical psychology, adopts AI tools slowly due to regulatory, privacy, and liability constraints; pilots for note-drafting exist but production-scale AI-authored clinical reports remain rare.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by organizing test data, drafting initial sections, suggesting structure, and flagging patterns, allowing clinicians to focus on interpretation and clinical synthesis. However, the assistance is moderate because clinicians still must substantially rewrite, verify, and integrate findings to meet clinical standards.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully speed up drafting, organizing data, and generating boilerplate report sections, letting the neuropsychologist focus on interpretation and clinical judgment.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can draft text and organize test data, neuropsychological reports require integrating complex clinical judgment, interpreting nuanced test patterns, synthesizing multimodal data (interviews, observations, self-report), and formulating diagnostic impressions that demand clinical expertise. Current AI cannot reliably perform these integrative and inferential aspects at the quality and reliability required for clinical use.
Task automatabilityclaude-sonnet-52/5While AI can draft narrative text from structured test data, synthesizing multi-source clinical findings into a valid diagnostic report requires clinical judgment about test validity, discrepancies, and integration that current systems cannot reliably perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Neuropsychological reports are typically authored by licensed neuropsychologists and used in diagnostic, treatment, and legal contexts where professional accountability and signature are legally and ethically required. Professional licensing, malpractice liability, and regulatory standards (APA, state licensing boards) create strong barriers to unsupervised automation.
Adoption barriersclaude-sonnet-55/5Clinical neuropsychological reports require a licensed psychologist's professional judgment and signature, with strict liability, ethical, and regulatory obligations preventing full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI text generation is cheap per token, but the oversight required—clinician review, correction, and verification of all findings—makes the all-in cost per completed, clinically acceptable report comparable to partial human authoring. The human still bears liability and does substantial work.
Cost vs. human wageclaude-sonnet-52/5AI drafting could reduce transcription/writing time cheaply, but the required clinician review, liability, and integration oversight keep total cost close to human-comparable rather than order-of-magnitude cheaper.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed clinical product reliably generates standalone neuropsychological reports suitable for clinical or legal use. Some AI drafting tools exist for medical text, but they lack the domain-specific validation, error-checking, and liability frameworks necessary for neuropsychological documentation, where errors carry serious diagnostic and medicolegal consequences.
Technical feasibility todayclaude-sonnet-52/5Some EHR and dictation tools offer AI-assisted drafting or summarization, but no deployed product independently produces validated neuropsychological reports in clinical practice at scale.

Provide education or counseling to individuals and families.

19

CI 14–25 · exposure 17 · augmentation 63 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Healthcare and neuropsychological practice remain heavily regulated, risk-averse sectors with slow AI adoption in direct clinical roles. Pilot use of AI for psychoeducational materials exists, but production replacement of counselor-led education is minimal and strongly resisted by licensing bodies and professional standards.
Sector adoption velocityclaude-sonnet-52/5Healthcare, especially clinical neuropsychology, is a slower-adopting sector for patient-facing AI due to regulatory, ethical and trust concerns, with pilots more common than deployment.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by drafting psychoeducational handouts, summarizing cognitive test results for explanation, or suggesting talking points for family meetings, thus allowing the neuropsychologist to spend more time on active listening and therapeutic engagement. This moderate augmentation does not eliminate the human clinician's role.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist by drafting patient education materials, summarizing conditions in accessible language, and helping prepare counseling session content, boosting clinician efficiency.
Task automatabilityclaude-haiku-4-5-202510011/5Providing education and counseling requires sustained human rapport, emotional attunement, adaptive response to individual circumstances, and therapeutic presence that current AI cannot replicate end-to-end. While AI can generate informational content or draft talking points, it cannot substitute for the relational and clinical judgment central to effective counseling.
Task automatabilityclaude-sonnet-52/5Providing counseling/education involves empathetic, adaptive dialogue tailored to patients' emotional states and cognitive conditions, which current AI cannot reliably replicate end-to-end at equal quality despite drafting help.
Adoption barriersclaude-haiku-4-5-202510014/5Licensed neuropsychologists are legally and ethically bound to provide direct care; counseling and education to vulnerable populations (patients with neurological conditions and families) carries regulatory requirements, professional licensing, and liability exposure that prevent substitution by AI-only systems. Clinical judgment and accountability remain the provider's responsibility.
Adoption barriersclaude-sonnet-54/5Clinical counseling typically requires licensed professional judgment, liability accountability, and often direct human interaction, especially for vulnerable neuropsych patients and their families.
Cost vs. human wageclaude-haiku-4-5-202510012/5While AI content generation is inexpensive, integration into a safe counseling workflow, required human oversight, and liability costs approach or exceed the hourly cost of a neuropsychologist providing education directly. The human cannot be fully removed from the critical path.
Cost vs. human wageclaude-sonnet-52/5While AI text generation is cheap, the human clinician still must deliver, personalize, and take liability for counseling, so cost savings are modest once oversight and liability are factored in.
Technical feasibility todayclaude-haiku-4-5-202510012/5Some AI systems can produce educational materials or psychoeducational scripts, but no deployed product reliably conducts actual counseling sessions with the nuance, safety oversight, and ethical accountability required in clinical neuropsychology. Chatbots exist but carry unacceptable error and liability risks for therapeutic contexts.
Technical feasibility todayclaude-sonnet-52/5Chatbot-based mental health/education tools exist but are narrow, unregulated for clinical neuropsych counseling, and not integrated into standard practice as a replacement for clinician-led counseling.

Conduct neuropsychological evaluations such as assessments of intelligence, academic ability, attention, concentration, sensorimotor function, language, learning, and memory.

18

CI 15–20 · exposure 20 · augmentation 50 · importance 4.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption of AI tools in neuropsychology is nascent and limited to scoring aids and research prototypes; most clinical neuropsychology practices remain human-centered with slow integration of computational tools, reflecting both regulatory caution and the specialty's emphasis on direct patient interaction.
Sector adoption velocityclaude-sonnet-52/5Healthcare and clinical psychology adopt AI slowly due to regulatory, liability, and patient-safety concerns, with pilots for digital cognitive screening but limited production deployment for full evaluations.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist by automating test scoring, flagging patterns in large datasets, and organizing results for interpretation, which can save time on administrative tasks; however, the core clinical work—patient interaction, adaptive administration, and syndrome formulation—remains human-driven.
Augmentation potentialclaude-sonnet-53/5AI can assist with scoring, normative comparisons, report drafting, and flagging patterns in test data, meaningfully aiding the neuropsychologist without replacing direct assessment.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist in scoring structured tests and analyzing some cognitive patterns, neuropsychological evaluation requires direct clinical observation, adaptive test administration based on patient responses, and integration of behavioral data that current systems cannot reliably perform end-to-end. The human psychologist's dynamic interaction with the patient—adjusting difficulty, interpreting effort level, detecting malingering—remains essential and not automatable to 50% time-saving equivalence.
Task automatabilityclaude-sonnet-52/5While scoring and some interpretation could be assisted by AI, the actual conduct of standardized neuropsychological testing requires direct clinical observation, rapport-building, and adaptive administration that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Neuropsychological evaluation is legally and ethically regulated; a licensed clinical or neuropsychologist must administer tests, interpret results in the patient's context, and produce a clinical report that stands as a legal/diagnostic document. Scope-of-practice laws and professional liability requirements create hard barriers to full automation.
Adoption barriersclaude-sonnet-55/5Neuropsychological evaluation is a licensed clinical activity requiring a doctoral-level psychologist, with strong legal, ethical, and reimbursement requirements for human authorization of diagnostic conclusions.
Cost vs. human wageclaude-haiku-4-5-202510012/5Neuropsychological evaluation by a licensed psychologist costs $2,000–$5,000+ per full battery. Current AI tools are inexpensive for scoring but do not replace the evaluation itself; human oversight and clinical interpretation dominate total cost, making AI assist much cheaper than standalone but not orders of magnitude cheaper per complete evaluation.
Cost vs. human wageclaude-sonnet-52/5AI-assisted scoring software is cheap, but the overall evaluation still requires a licensed neuropsychologist's time for administration and interpretation, keeping costs comparable to human-delivered care.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably conducts full neuropsychological evaluations independently. AI scoring tools exist for some standardized test batteries (e.g., WAIS, MMSE), but these require human administration and clinical judgment; no end-to-end system performs the full evaluation workflow in production clinical settings.
Technical feasibility todayclaude-sonnet-51/5No deployed product administers full neuropsychological batteries autonomously; digital cognitive testing tools exist but are narrow adjuncts, not replacements for clinician-administered evaluation.

Interview patients to obtain comprehensive medical histories.

15

CI 5–25 · exposure 17 · augmentation 63 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Healthcare, particularly specialty mental health and neuropsychology, remains highly regulated and conservative in clinical adoption; despite digitization, autonomous patient interview systems have not been adopted in production practice, and organizational inertia is strong.
Sector adoption velocityclaude-sonnet-52/5Healthcare, especially specialized clinical assessment fields like neuropsychology, adopts AI slowly due to regulatory, liability, and workflow integration challenges, with pilots more common than deployed autonomous systems.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by transcribing interviews, summarizing patient narratives, flagging potential red flags in responses, or generating preliminary checklists, modestly raising clinician efficiency; however, the core diagnostic and rapport-building work remains human-dependent.
Augmentation potentialclaude-sonnet-54/5AI tools can meaningfully assist by pre-populating intake forms, transcribing and summarizing interviews, flagging inconsistencies, and organizing history data, significantly aiding the clinician while they retain interview control.
Task automatabilityclaude-haiku-4-5-202510011/5Obtaining comprehensive medical histories requires subtle interpersonal judgment, follow-up probing based on patient responses, and clinical reasoning to assess symptom significance—all requiring nuanced human understanding that current AI cannot reliably perform end-to-end without substantial human oversight.
Task automatabilityclaude-sonnet-52/5Structured history-taking can be partially supported by AI intake forms or chatbots, but neuropsychological histories require adaptive follow-up questions, clinical judgment, and building rapport with cognitively impaired patients, which current AI cannot fully replicate end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Medical interviews are legally and ethically bound to licensed clinicians; patient confidentiality, informed consent, and duty of care create hard regulatory and liability barriers that prevent autonomous AI deployment, and the human clinician must conduct or directly oversee the intake.
Adoption barriersclaude-sonnet-54/5Obtaining a clinical medical history for neuropsychological evaluation typically requires a licensed professional to interpret responses, assess reliability, and integrate findings into diagnosis, creating strong regulatory and liability barriers to full automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5A neuropsychologist's loaded cost is high, but the AI systems that could partially assist (speech recognition, summarization) still require extensive clinician review and cannot replace the interview itself, making the all-in cost still favoring human performance.
Cost vs. human wageclaude-sonnet-52/5While AI-assisted intake tools are cheap, the oversight, verification, and clinical follow-up needed to ensure accuracy for a neuropsychological history keeps overall cost comparable to or only modestly below clinician-led interviews.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can generate templates or prompt structures for history-taking, no deployed product reliably conducts independent patient interviews with the sensitivity, adaptability, and clinical accuracy required in neuropsychological practice; this remains research-adjacent.
Technical feasibility todayclaude-sonnet-52/5Some healthcare systems deploy AI-driven intake questionnaires or symptom checkers, but no product reliably conducts full comprehensive neuropsychological history interviews independently in production.

Design or implement rehabilitation plans for patients with cognitive dysfunction.

14

CI 3–25 · exposure 13 · augmentation 63 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Healthcare remains a cautious adopter of autonomous AI, with pilots common but production-scale replacement rare; neuropsychology specifically involves high-stakes decisions about vulnerable populations, slowing AI adoption in this specialist domain.
Sector adoption velocityclaude-sonnet-52/5Healthcare, especially specialized clinical neuropsychology, has slower and more cautious AI adoption compared to information/finance sectors, with pilots more common than production use.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist by suggesting evidence-based interventions, organizing patient history, and flagging relevant research, raising productivity in planning tasks while the neuropsychologist maintains clinical judgment and final decision authority.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist by analyzing assessment data, suggesting exercise regimens, tracking patient progress, and drafting plan templates, enhancing clinician efficiency while the clinician retains decision authority.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist with literature review and suggest evidence-based interventions, designing and implementing personalized rehabilitation plans requires real-time clinical judgment, patient interaction, and adaptive modification based on individual neurological status—tasks that current AI cannot reliably perform end-to-end to meet the 50% time-saving threshold.
Task automatabilityclaude-sonnet-51/5Designing individualized rehabilitation plans requires clinical judgment integrating assessment data, patient history, and dynamic response to therapy that current AI cannot perform end-to-end without a licensed clinician driving the process.'
Adoption barriersclaude-haiku-4-5-202510014/5Strong regulatory and liability barriers exist: neuropsychologists must be licensed, legally responsible for treatment plans, and accountable for patient outcomes; healthcare regulations require qualified professionals to design and sign off on rehabilitation interventions, creating hard requirements for human oversight.
Adoption barriersclaude-sonnet-55/5Rehabilitation planning for cognitive dysfunction is a licensed clinical activity requiring a credentialed neuropsychologist's judgment and sign-off, with strong liability and regulatory constraints.
Cost vs. human wageclaude-haiku-4-5-202510012/5Current AI assistive tools are still relatively expensive to integrate into clinical workflows and require ongoing expert oversight, making them comparable to or more costly than the time savings they provide compared to specialist neuropsychologist labor.
Cost vs. human wageclaude-sonnet-51/5Given liability, complexity, and the need for licensed oversight, AI cannot yet replace the clinician's cost in this task, making all-in AI costs comparable or higher when accounting for required human review.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed products reliably design or implement complete rehabilitation plans; existing AI tools offer decision support or protocol templates but remain advisory rather than autonomous, with neuropsychologists retaining final design and implementation authority.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously designs or implements neuropsychological rehabilitation plans; existing digital cognitive-training tools are adjuncts, not substitutes for clinician-led planning.

Conduct research on neuropsychological disorders.

14

CI 11–16 · exposure 5 · augmentation 63 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Academic research sectors adopt AI tools slowly; neuropsychology remains highly human-centric with long publication cycles and institutional inertia; AI use remains at pilot and tool level (analysis software) rather than autonomous research agents.
Sector adoption velocityclaude-sonnet-52/5Academic and clinical research settings adopt AI tools slowly and unevenly, mostly for literature review and data processing, with limited integration into the core research and experimental design process.
Augmentation potentialclaude-haiku-4-5-202510013/5AI tools can meaningfully assist neuropsychologists with literature synthesis, statistical workflows, and data visualization, but these augmentations support rather than transform core research activities like study design and interpretation of disorder mechanisms.
Augmentation potentialclaude-sonnet-54/5AI significantly aids literature reviews, data analysis, drafting manuscripts, and identifying patterns in neuropsychological data, meaningfully boosting researcher productivity while humans retain control over design and interpretation.
Task automatabilityclaude-haiku-4-5-202510011/5Neuropsychological research requires novel hypothesis generation, experimental design decisions, interpretation of complex behavioral and neuroimaging data, and synthesis into publishable findings—all tasks demanding domain expertise and creativity that current AI cannot perform end-to-end at the quality and novelty standards expected in peer-reviewed research.
Task automatabilityclaude-sonnet-51/5Research on neuropsychological disorders requires original hypothesis generation, experimental design, human subject interaction, ethical oversight, and interpretation of novel findings—none of which current AI can execute end-to-end at equal quality.
Adoption barriersclaude-haiku-4-5-202510014/5Research ethics review boards (IRBs) maintain legal and regulatory oversight of human subjects research; funding agencies and peer review require human researcher accountability; publication requires human expert sign-off—all hard barriers to autonomous substitution of the research role.
Adoption barriersclaude-sonnet-54/5Research involving human subjects with neurological/psychological disorders is governed by IRB ethics approval, clinical licensure for assessment components, and publication/peer-review norms requiring accountable human authorship.
Cost vs. human wageclaude-haiku-4-5-202510012/5While AI can reduce some research labor costs (literature screening, data analysis), the expert neuropsychologist's judgment, hypothesis formulation, and grant-writing cannot be replaced, and oversight of AI outputs adds cost rather than reducing it below human wages.
Cost vs. human wageclaude-sonnet-52/5AI can cheaply assist with literature synthesis or statistical analysis, but the overall research process still requires costly human expertise, participant recruitment, and clinical judgment, keeping total cost comparable to or only modestly less than fully human-led research.
Technical feasibility todayclaude-haiku-4-5-202510012/5AI can assist with literature review, data preprocessing, and statistical analysis, but no deployed product reliably performs the full research pipeline (design, execution, interpretation, novel insight generation) that defines neuropsychological disorder research in production settings.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously conducts neuropsychological research; AI tools are used piecemeal (literature review, data analysis) but not as a substitute for the researcher role.

Participate in educational programs, in-service training, or workshops to remain current in methods and techniques.

13

CI 0–25 · exposure 13 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5This is a mandatory, individual professional development requirement embedded in licensure and ethical standards. There is no sector-wide adoption dynamic for 'automating' professional learning; it remains uniformly a human responsibility across all neuropsychology practice settings.
Sector adoption velocityclaude-sonnet-52/5Healthcare and licensed professional fields adopt AI slowly for compliance-related activities, though e-learning platforms have long been used for CE credits.
Augmentation potentialclaude-haiku-4-5-202510012/5AI can assist by summarizing workshop materials, finding relevant continuing education options, or organizing learning resources, but the task itself—active, professional participation—requires human judgment and engagement that AI does not meaningfully augment.
Augmentation potentialclaude-sonnet-54/5AI can effectively curate relevant courses, summarize research literature, generate practice quizzes, and help track CE requirements, meaningfully boosting efficiency while the neuropsychologist remains in control.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires active human learning, professional judgment about relevance and application to clinical practice, and engagement with peers—capacities that current AI cannot replicate end-to-end. AI can assist with finding content but cannot genuinely 'participate' in professional development or internalize new clinical methods.
Task automatabilityclaude-sonnet-52/5AI can help curate content and summarize materials, but actual participation in training and workshops is a human activity requiring physical/virtual presence, engagement, and professional judgment that cannot be end-to-end automated.
Adoption barriersclaude-haiku-4-5-202510015/5Professional licensing boards require continuing education and currency in methods as a legal and ethical condition of practice. Neuropsychologists must personally complete these activities to maintain credentials; substitution or full delegation is not permitted.
Adoption barriersclaude-sonnet-54/5Licensing boards typically mandate continuing education hours be completed by the licensed professional personally, creating a strong regulatory barrier to substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5Neuropsychologists must personally invest time to maintain licensure and clinical competence; this is a non-delegable professional obligation. The cost of their participation time cannot be meaningfully compared to AI cost, as the human's presence is mandatory.
Cost vs. human wageclaude-sonnet-52/5AI tools can reduce time spent searching/summarizing materials cheaply, but the core task of attending and absorbing training still requires human time, limiting overall cost savings.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product can autonomously participate in educational programs, workshops, or in-service training on behalf of a neuropsychologist. This inherently requires human attendance, interaction, and professional decision-making about application.
Technical feasibility todayclaude-sonnet-52/5Existing products (e.g., learning platforms, AI tutors) support parts of continuing education but no deployed system autonomously completes an individual's professional development requirements.

Establish neurobehavioral baseline measures for monitoring progressive cerebral disease or recovery.

10

CI 0–20 · exposure 13 · augmentation 63 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Clinical neuropsychology remains a conservative, regulation-bound field with slow AI adoption. Few clinical neuropsychology practices have deployed autonomous AI for baseline setting; the field prioritizes human expertise and clinical accountability over automation.
Sector adoption velocityclaude-sonnet-52/5Healthcare and clinical neuropsychology are traditionally slow to adopt full automation due to regulatory, liability, and diagnostic accuracy concerns, though digital testing tools are gradually being piloted.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by automating test administration, scoring, and data visualization (e.g., normative comparisons, trend alerts), which can raise the neuropsychologist's efficiency in interpreting and establishing baselines. However, the assistance is partial and focused on support, not transformation of the core task.
Augmentation potentialclaude-sonnet-54/5AI-assisted computerized testing batteries, automated scoring, and pattern-recognition tools can meaningfully speed up data collection and flag abnormalities, enhancing the neuropsychologist's efficiency while they retain clinical judgment.
Task automatabilityclaude-haiku-4-5-202510011/5Establishing baseline neurobehavioral measures requires clinical judgment, real-time patient interaction, and synthesis of complex neuropsychological test results with patient history—tasks that demand human expertise and cannot be performed end-to-end by current AI systems. While AI might assist with test scoring or data organization, the baseline-setting decision itself is inherently clinical and requires a licensed neuropsychologist.
Task automatabilityclaude-sonnet-52/5This task requires administering and interpreting standardized neuropsychological tests, clinical judgment, and patient interaction; AI can assist with scoring and data aggregation but cannot independently establish valid clinical baselines end-to-end.'
Adoption barriersclaude-haiku-4-5-202510015/5Establishing neurobehavioral baselines is a regulated clinical function requiring a licensed neuropsychologist or supervised clinical psychologist in most jurisdictions; there are liability and error-cost asymmetries (misdiagnosis leading to wrong treatment). Legal and regulatory frameworks mandate human sign-off and accountability.
Adoption barriersclaude-sonnet-55/5This is a licensed clinical activity requiring a credentialed neuropsychologist to interpret results and establish a valid clinical baseline, with high liability for misdiagnosis.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of AI systems (specialized neuropsychological assessment software, oversight, validation) plus the neuropsychologist time still required to interpret and establish clinically valid baselines exceeds the cost of direct human performance of the task. The human's clinical judgment remains non-substitutable.
Cost vs. human wageclaude-sonnet-52/5Digital assessment tools reduce some administration/scoring costs, but the clinical interpretation and integration into a diagnostic baseline still requires expensive specialist labor, so overall savings are modest.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI product reliably performs the full task of establishing neurobehavioral baselines in clinical practice. AI tools exist for test administration and scoring, but baseline establishment—selecting appropriate measures, interpreting patterns, and making clinical recommendations—remains within neuropsychologist domain and is not yet a mature, autonomous AI function in production.
Technical feasibility todayclaude-sonnet-52/5Some digital cognitive testing platforms and automated scoring tools exist and are used clinically, but they augment rather than replace the neuropsychologist's clinical interpretation and baseline determination.

Diagnose and treat conditions such as chemical dependency, alcohol dependency, Acquired Immune Deficiency Syndrome (AIDS) dementia, and environmental toxin exposure.

9

CI 3–16 · exposure 13 · augmentation 50 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Healthcare, especially behavioral health and neuropsychology, adopts AI slowly due to regulatory constraints, liability concerns, and entrenched clinical workflows. While screening tools see some deployment, autonomous diagnostic and treatment AI in neuropsychology remains pilot-stage in most organizations.
Sector adoption velocityclaude-sonnet-52/5Healthcare and clinical psychology are moderate-to-slow adopters of AI for diagnostic decision-making due to regulatory, liability, and trust constraints, though administrative AI use is growing.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist neuropsychologists by analyzing cognitive test batteries, flagging patterns in patient data, and summarizing relevant literature, moderately raising their efficiency while the clinician retains diagnostic and treatment decision authority.
Augmentation potentialclaude-sonnet-53/5AI can assist with literature review, symptom pattern analysis, documentation, and flagging risk factors, but the clinician must independently perform diagnosis and treatment planning.
Task automatabilityclaude-haiku-4-5-202510012/5While AI can assist in analyzing test results and identifying patterns in patient data, diagnosis and treatment of complex neuropsychological conditions require integrated clinical judgment, patient rapport, adaptive treatment planning, and monitoring that current AI cannot reliably perform end-to-end. The task involves nuanced assessment of cognitive, behavioral, and social factors that exceed 50% time savings at equal quality.
Task automatabilityclaude-sonnet-51/5Diagnosing and treating complex neuropsychological conditions requires clinical judgment, physical/neurological examination, patient rapport, and legal authority to treat—no current AI system can perform this end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Neuropsychological diagnosis and treatment are exclusively licensed activities requiring state licensure, credential oversight, and direct clinical responsibility. Legal and regulatory frameworks mandate that licensed neuropsychologists conduct and sign off on diagnoses, creating hard barriers to full automation.
Adoption barriersclaude-sonnet-55/5Diagnosis and treatment of chemical dependency, AIDS dementia, and toxin exposure require licensed medical/psychological authority, with high liability and legal requirements for human sign-off.
Cost vs. human wageclaude-haiku-4-5-202510011/5The all-in cost of integrating AI systems for diagnostic support, combined with required human oversight, verification, and liability management, currently exceeds the cost of direct neuropsychologist assessment given the high stakes of misdiagnosis in these conditions.
Cost vs. human wageclaude-sonnet-51/5Since AI cannot perform the licensed diagnostic and treatment functions at all, there is no viable substitute cost comparison—human clinician cost is unavoidable.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed product reliably performs independent diagnosis and treatment of neuropsychological conditions like AIDS dementia or chemical dependency. AI tools exist for screening and data analysis, but actual clinical diagnosis and treatment planning remain dependent on human neuropsychologists in all mature clinical settings.
Technical feasibility todayclaude-sonnet-51/5No deployed product independently diagnoses or treats these conditions in clinical practice; AI tools at best support documentation or research, not the core diagnostic/treatment act.

Consult with other professionals about patients' neurological conditions.

8

CI 0–16 · exposure 13 · augmentation 63 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Healthcare, particularly neuropsychology and inter-professional consultation, remains a laggard sector for task automation due to regulatory constraints, liability concerns, and the irreducible requirement for human clinical judgment and accountability.
Sector adoption velocityclaude-sonnet-52/5Healthcare, especially specialized clinical consultation, adopts AI slowly due to regulatory, liability, and trust constraints, with most current use limited to documentation support.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist by generating case summaries, organizing patient history, or drafting talking points before a consultation call, but the actual consultation remains a human-to-human professional interaction where AI plays a supporting rather than transformative role.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist by summarizing patient records, literature, and test results, or drafting consultation notes, improving efficiency while the neuropsychologist retains full decision-making role.
Task automatabilityclaude-haiku-4-5-202510011/5Consulting with other professionals requires nuanced exchange of patient information, clinical judgment, and context-dependent discussion. Current AI cannot reliably conduct genuine two-way professional consultation that meets medical standards; it can only draft templated communications or summarize cases.
Task automatabilityclaude-sonnet-52/5This requires synthesizing complex clinical judgment, live dialogue, and relationship-based trust that current AI cannot replicate end-to-end, though AI can help prepare materials for such consultations.rns
Adoption barriersclaude-haiku-4-5-202510015/5Medical consultation between licensed professionals carries strict regulatory and liability requirements. Neuropsychologists must consult directly with other licensed practitioners; a non-licensed AI intermediary would violate professional standards, medical-legal requirements, and clinical accountability expectations.
Adoption barriersclaude-sonnet-55/5Clinical consultations on patient neurological conditions require licensed professional judgment, carry high liability, and are protected by medical/legal scope-of-practice requirements.
Cost vs. human wageclaude-haiku-4-5-202510011/5The consultation task involves licensed professionals whose time is not fundamentally cheaper to replace with AI inference; oversight, liability review, and human verification would still be required, making net cost comparable or higher than the professional's own time.
Cost vs. human wageclaude-sonnet-51/5Since AI cannot substitute for the consultation itself, there is no viable cost comparison; a licensed neuropsychologist's judgment remains necessary and costlier alternatives don't exist.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can generate consultation drafts or summaries, no deployed system reliably performs actual professional consultation end-to-end. Neurologists, radiologists, and other specialists require real-time exchange and accountability that AI cannot provide in production medical settings.
Technical feasibility todayclaude-sonnet-51/5No deployed product autonomously conducts professional-to-professional clinical consultations about neurological conditions in production settings today.

Diagnose and treat conditions involving injury to the central nervous system, such as cerebrovascular accidents, neoplasms, infectious or inflammatory diseases, degenerative diseases, head traumas, demyelinating diseases, and various forms of dementing illnesses.

4

CI 0–7 · exposure 5 · augmentation 63 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Neuropsychology operates in traditional healthcare and academic medical centers with slow digitization; clinical adoption of AI for autonomous diagnosis remains minimal, with only limited AI-assisted pilot programs in large academic institutions.
Sector adoption velocityclaude-sonnet-52/5Healthcare broadly adopts AI slowly for diagnostic/treatment decisions due to regulatory and liability constraints, though imaging-adjacent tools are gaining traction.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can usefully assist neuropsychologists through automated cognitive test scoring, neuroimaging analysis flagging, medical literature synthesis, and differential diagnosis suggestion, meaningfully raising productivity while the clinician retains diagnostic authority.
Augmentation potentialclaude-sonnet-54/5AI can meaningfully assist via literature review, imaging analysis support, documentation, and differential diagnosis suggestions, improving efficiency while the clinician retains responsibility.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires integrated clinical judgment synthesizing patient history, neurological examination findings, imaging interpretation, and longitudinal cognitive assessment—all demanding real-time human observation and dynamic hypothesis testing that current AI cannot perform end-to-end with 50% time savings at equal quality.
Task automatabilityclaude-sonnet-51/5This requires direct clinical diagnosis and treatment decisions involving physical examination, patient history, and complex judgment about serious neurological conditions—no current AI system performs this end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Diagnosis and treatment of CNS disease requires a licensed neuropsychologist or physician; legal, regulatory, and liability frameworks mandate human licensure and accountability, creating hard barriers to substitution regardless of technical capability.
Adoption barriersclaude-sonnet-55/5Diagnosing and treating neurological disease legally requires a licensed physician/neuropsychologist, with high liability and mandatory human accountability for clinical decisions.
Cost vs. human wageclaude-haiku-4-5-202510011/5AI inference and oversight for neuropsychological diagnosis remain substantially cheaper than a neuropsychologist's time on a per-hour basis, but the clinical liability, required human review, and inability to replace the licensed clinician's judgment make the all-in cost comparison unfavorable for automation.
Cost vs. human wageclaude-sonnet-51/5AI cannot substitute for the licensed clinical work involved, so there is no valid AI-only cost comparison; any AI use requires expensive human oversight, making it not cheaper overall.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI tools assist with narrow components (e.g., imaging segmentation, literature search), no deployed product reliably performs the full diagnostic and treatment planning process independently; clinical neuropsychology diagnosis remains human-centric in all major healthcare systems.
Technical feasibility todayclaude-sonnet-51/5No deployed product independently diagnoses and treats CNS conditions; AI tools exist only as decision-support for imaging or documentation, not as autonomous diagnosticians/treaters.

Diagnose and treat pediatric populations for conditions such as learning disabilities with developmental or organic bases.

4

CI 0–7 · exposure 5 · augmentation 50 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Healthcare, particularly pediatric mental health and neurodevelopmental services, remains heavily regulated and human-contact dependent. Adoption of AI for core diagnostic and treatment tasks in this population is minimal; organizations prioritize clinical validation and liability mitigation over automation.
Sector adoption velocityclaude-sonnet-52/5Healthcare and pediatric clinical practice adopt AI slowly due to regulatory, liability, and safety concerns, with AI use limited mostly to administrative or scoring support rather than core diagnostic/treatment decisions.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist neuropsychologists by automating test scoring, organizing research, summarizing patient history, and flagging patterns in data; however, the interpretive and therapeutic core—clinical judgment, patient engagement, treatment selection—remains human-driven, offering moderate but not transformative augmentation.
Augmentation potentialclaude-sonnet-53/5AI can assist with test scoring, report drafting, literature review, and flagging patterns in assessment data, improving efficiency while the neuropsychologist retains full clinical responsibility.
Task automatabilityclaude-haiku-4-5-202510011/5Diagnosing and treating pediatric neuropsychological conditions requires clinical judgment, patient interaction, differential diagnosis across complex organic and developmental etiologies, and individualized treatment planning that current AI systems cannot perform end-to-end. While AI may assist with test scoring or literature review, the core diagnostic and therapeutic work—especially with vulnerable pediatric patients—remains beyond automation.
Task automatabilityclaude-sonnet-51/5Diagnosing and treating pediatric neuropsychological conditions requires clinical judgment, hands-on assessment, rapport-building with children, and integration of complex developmental history that current AI cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510015/5Diagnosis and treatment of pediatric neuropsychological conditions are restricted to licensed clinical neuropsychologists or physicians in nearly all jurisdictions; malpractice liability is severe; informed parental consent is legally required; and regulatory bodies (state licensing boards, FDA for neuropsychological devices) impose hard barriers on autonomous clinical decision-making affecting minors.
Adoption barriersclaude-sonnet-55/5Diagnosis and treatment of pediatric medical/psychological conditions require licensure, direct patient contact, and legal/ethical accountability that only a credentialed neuropsychologist can provide.
Cost vs. human wageclaude-haiku-4-5-202510011/5The loaded cost of a neuropsychologist ($100k+ salary, licensure, liability insurance, malpractice coverage) far exceeds any current AI system cost when factoring in the extensive oversight, validation, and human review required for clinical decisions affecting children.
Cost vs. human wageclaude-sonnet-51/5AI cannot substitute for the clinical service itself, so there is no meaningful cost comparison—any AI use is a supplement, not a replacement, to the licensed provider's fee.
Technical feasibility todayclaude-haiku-4-5-202510012/5No deployed AI product reliably performs pediatric neuropsychological diagnosis or treatment planning independently. AI tools exist for screening and ancillary tasks (e.g., test administration support), but production systems do not conduct full diagnostic evaluations or deliver treatment to pediatric populations at clinical standards.
Technical feasibility todayclaude-sonnet-51/5No deployed product independently diagnoses or treats pediatric learning disabilities; this remains firmly in the domain of licensed clinicians conducting in-person evaluation.

Educate and supervise practicum students, psychology interns, or hospital staff.

4

CI 0–7 · exposure 5 · augmentation 38 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Clinical and academic institutions have strong regulatory and professional norms requiring human supervisors; adoption of AI automation in this space is nearly non-existent because the legal and ethical frameworks demand human accountability and presence.
Sector adoption velocityclaude-sonnet-52/5Healthcare and clinical training settings adopt AI slowly for supervisory/educational roles due to licensing, liability, and accreditation constraints, despite some AI adoption in adjacent administrative tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI can help organize case materials, generate template feedback, or flag knowledge gaps in trainees' work, but these are marginal assists; the core supervisory relationship and judgment-based teaching remain firmly human-driven.
Augmentation potentialclaude-sonnet-53/5AI can assist by providing training materials, case simulations, feedback on written reports, or supplementary educational content, but the core supervisory relationship and judgment remain human-led.
Task automatabilityclaude-haiku-4-5-202510011/5Educating and supervising trainees requires real-time interaction, relationship-building, adaptive feedback calibrated to individual developmental needs, and modeling of clinical judgment—tasks that demand human presence and contextual responsiveness that current AI cannot replicate end-to-end.
Task automatabilityclaude-sonnet-51/5Educating and supervising trainees requires personalized mentorship, real-time clinical judgment assessment, and relationship-building that AI cannot perform end-to-end today. Supervision involves legal/ethical accountability that must remain with a licensed human.
Adoption barriersclaude-haiku-4-5-202510015/5Clinical supervision and practicum oversight are legally mandated functions requiring a licensed, credentialed human supervisor; many state boards and accreditation bodies explicitly require direct supervision by a qualified practitioner, making AI substitution legally impermissible.
Adoption barriersclaude-sonnet-55/5Clinical supervision of trainees is typically legally mandated to be performed by licensed, credentialed professionals, with strict accreditation and liability requirements that preclude AI substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5A neuropsychologist's time spent supervising trainees is high-value clinical and educational labor; AI assistance with materials or documentation does not yet approach cost parity with a qualified supervisor's hourly rate, and cannot substitute for the supervisor role.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute product to compare costs against; the human supervisor's cost is not displaceable by any current AI offering for this task.
Technical feasibility todayclaude-haiku-4-5-202510012/5While AI can generate educational materials and draft feedback, no deployed system reliably performs the core supervisory and mentoring functions (observing performance, providing corrective guidance, assessing readiness to practice independently) at the standard required for clinical training.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product performs clinical supervision or educates practicum students/interns in neuropsychology; this remains firmly a human role in practice.

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How to read this

A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.

What would change this score

New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.