Application of artificial intelligence for diagnosis, prognosis and treatment in psychology: a review
Bibliographic record
Abstract
Over the past few decades, technological and scientific developments have shaped our understanding of underlying characteristics of human neurobiology, emotions, thoughts and their association with psychological illnesses.However, despite the advances in the discipline there is widespread disappointment with the progress in diagnosing, predicting, and treating psychological disorders.At present we have various approaches available in form of interview, observation and psychometric tools for assessment of clients with mental health problems.Mostly psychometric questionnaires are used for diagnosis, but these are often unreliable, imprecise or unable to provide a consistent assessment of the symptomology of the clients.However, these restrictions can be overcome using artificial intelligence (AI) and its approaches.In psychology, AI is a general term that may be considered as usage of computer-related technology and algorithms for diagnosis, prognosis, prevention, and treatment of mental health problems.The idea of amalgamating the complexities of psychology and dynamism of AI has gained momentum in recent times.Since a decade or so ago, there has been an increase in application of AI in the profession of medicine; however, psychology still needs a lot of work for effectiveness.This article provides a review on the application of AI in important functions of psychology in the clinical field: diagnosis, prediction, and treatment of psychological disorders.It also focuses on the challenges and limitations of AI in psychological practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".