Artificial intelligence.pptx study material
- 2. History For many people, artificial intelligence (AI) brings to mind some form of humanoid robot that speaks andacts like a human. AI is much more than merely robotics and machines. Professor John McCarthy of Stanford University, who first coined the term “artificial intelligence” in the early 1950s, defined it as “the science and engineering of making intelligent machines, especially intelligent computer programs”; he defined intelligence as “the computational part of the ability to achieve goals”
- 3. History English Mathematician Alan Turing is considered one of the forefathers of AI research, and devised the first test to determine if a computer program was intelligent. AI has established itself as an integral part of medicine and psychiatry.
- 4. Thesemantics of AI Two subsets of AI are machine learning and deep learning. Machine learning is defined as a set of methods that can automatically detect patterns in data and then use the uncovered pattern to predict future data. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.
- 5. Thesemantics of AI Machine learning can be supervised, semi-supervised, or unsupervised. The majority of practical machine learninguses supervised learning, where all data are labeled and an algorithm is used to learn the mapping function from the input to the output. In unsupervised learning, all data are unlabeled and the algorithm models the underlying structure of the data by itself. Semi-supervised learning is a mixture of both.
- 6. Types of AI General or “strong” AI, and narrow or “weak” AI. Strong AI is defined as computers that can think on a level at least equal to humans and are able to experience emotions and even consciousness. Weak AI includes adding “thinking-like” features to computers to make them more useful tools. Almost all AI technologies available today are considered to be weak AI.
- 7. How can AIbe usedin psychiatry? Artificially intelligent technologies have been used in psychiatry for several decades. One of the earliest examples is ELIZA, a computer program published by Professor Joseph Weizenbaum of the Massachusetts Institute of Technology in 1966. ELIZA consisted of a language analyzer and a script or a set of rules to improvise around a certain theme; the script DOCTOR was used to simulate a Rogerian psychotherapist.
- 8. UstMA,PanL,CherkasskyVL,etal. Machinelearningofneural representationsofsuicideandemotion conceptsidentifiessuicidalyouth.Nat HumBehav.2017;1:911-919. A recent study examined AI’s ability to distinguish between an individual who had suicidal ideation vs a control group. Machine-learning algorithms were used to evaluate functional MRI scans of 34 participants (17 who had suicidal ideation and 17 controls) to identify certain neural signatures of conc The machine-learning algorithms were able to distinguish between these 2 groups with 91% accuracy. They also were able to distinguish between individuals who attempted suicide and those who did not with 94% accuracyypts related to life and death.
- 9. Astudyfrom theUniversityof Cincinnati Looked at using machine learning and natural language processing to distinguish genuine suicide notes from “fake” suicide notes that had been written by a healthy control group. Sixty-six notes were evaluated and categorized by 11 mental health professionals (psychiatrists, social workers, and emergency medicine physicians) and 31 PGY-3 residents.The accuracy of their results was compared with that of 9 machine-learning algorithms. The best machine-learning algorithm accurately classified the notes 78% of the time, compared with 63% of the time for the mental health professionals and 49% of the time for the residents.
- 10. WalshCG,RibeiroJD,FranklinJC.Predictingrisk ofsuicide attemptsovertimethroughmachine learning.Clinical PsychologicalScience. 2017;5(3):457-469. Researchers at Vanderbilt University examined using machine learning to predict suicide risk.23 They developed algorithms to scan electronic health records of 5,167 adults, 3,250 of whom had attempted suicide. In a review of the patients’ data from 1 week to 2 years before the attempt, the algorithms looked for certain predictors of suicide attempts, including recurrent depression, psychotic disorder, and substance use. The algorithm was 80% accurate at predicting whether a patient would attempt suicide within the next 2 years, and 84% accurate at predicting an attempt within the next week.
- 11. Prospectivestudy, Cincinnati Children’sHospital Used a machine-learning algorithm to evaluate 379 patients who were categorized into 3 groups: suicidal, mentally ill but not suicidal, or controls. All participants completed a standardized behavioral rating scale and participated in a semi-structured interview. Based on the participants’ linguistic and acoustic characteristics, the algorithm was able to classify them into the 3 groups with 85% accuracy.
- 12. CorcoranCM,CarrilloF,Fernández-SlezakD,et al. Predictionofpsychosisacrossprotocolsand riskcohorts usingautomatedlanguageanalysis. WorldPsychiatry. 2018;17(1):67-75. Many studies have looked at using language analysis to predicting the risk of psychosis in at-risk individuals. In one study, researchers evaluated individuals known to be at high risk for developing psychosis, some of whom eventually did develop psychosis. Participants were asked to retell a story and to answer questions about that story. Researchers fed the transcripts of these interviews into a language analysis program that looked at semantic coherence, syntactic complexity, and other factors. The algorithm was able to predict the future occurrence of psychosis with 82% accuracy. Participants who converted to psychosis had decreased semantic coherence and reduced syntactic complexity.
- 13. BediG,CarrilloF ,CecchiGA,etal.Automated analysisof freespeechpredictspsychosisonset inhigh-riskyouths.NPJ Schizophr . 2015;1:15030.doi:10.1038/npjschz.2015.30. 34 at-risk youth in an attempt to predict who would develop psychosis based on speech pattern analysis. The participants underwent baseline interviews and were assessed quarterly for 2.5 years.The algorithm was able to predict who would develop psychosis with 100% accuracy