Bibliographic record
Abstract
Artificial Intelligence (AI) has taken the world by storm, and like the impact of the internet we saw in the 1990s, it is reshaping the world we live in dramatically.Commonly defined as the ability of computers to perform tasks that are commonly associated with human beings, the definition assumes AI's ability to "learn" and generate answers.However, while there are some similarities in the learning process, there are many more differences, and some of these are key in its impact on Psychiatry as a field.It is indeed a complex area that covers cognition, executive functioning, and judgment, amongst others, but with emotional health being a major component.Emotions cover everything from joy and happiness to misery and sadness and the many different hues in between.How does one tease out the subtle differences between sadness with intact reactivity and sadness with loss of reactivity?Or hostility with little emotional expression and hostility with sarcasm?Are there biological correlates for each of these emotional states, or are they just "normal" expressions of the human mind?We know well how passing an examination gives us joy while failure provides us with a sense of sadness and despondence.There are emotional states that can be very subtle and are not often elicited in routine clinical examinations due to time constraints or cultural factors.A recent study on the use of ChatGPT with a team in Chennai was very revealing about the power of AI in coming to an accurate diagnosis in psychiatry based on DSM, but it was not as impressive in areas that required "complex" learning based on emotional needs when looking at recommendations.[1] As with all technologies, one needs to keep in mind its rapid evolution, and given its ability to learn, the growth will likely be exponential.Whether it will serve to replace individuals who work in the field by becoming easily available or being non-judgmental, less expensive, easily accessible with a smartphone, and with no risk of countertransference is something we can only wait and see, but also it's an area we need to study actively.What will be fascinating is to see if machine learning results in AI developing counter transference in the course of its 'learning' from humans.However, this growth in technology will need safeguards in place to protect it from being misused.As with the internet and the predatory behavior we have seen online, AI can both be a boon and a problem.Internet and telephone scams have become the bane of many countries, along with identity theft.Voice recognition software can easily be used to impersonate people but can also be used in grief work and therapy.AI creates a platform, on the other hand, unlike any we have seen before and can be used to target the vulnerable, as we saw in the 'blue whale challenge' .We need to recognize that the www.archivesbiologicalpsychiatry.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".