Artificial Intelligence in Behavioral and Mental Health Care by David D. Luxton

By David D. Luxton

Artificial Intelligence in Behavioral and psychological health and wellbeing Care

summarizes fresh advances in man made intelligence because it applies to psychological future health scientific perform. every one bankruptcy presents a technical description of the improvement, evaluation of software in medical perform, and empirical facts on medical efficacy.

In addition, each one bankruptcy incorporates a dialogue of sensible concerns in scientific settings, moral issues, and barriers of use. The e-book encompasses AI dependent advances in decision-making, in review and therapy, in delivering schooling to consumers, robotic assisted job of entirety, and using AI for study and knowledge gathering.

This ebook may be of use to psychological future health practitioners attracted to studying approximately, or incorporating AI advances into their perform and for researchers drawn to a accomplished assessment of those advances in a single source.

  • Summarizes AI advances to be used in psychological overall healthiness practice
  • Includes advances in AI dependent decision-making and consultation
  • Describes AI functions for evaluation and treatment
  • Details AI advances in robots for scientific settings
  • Provides empirical information on scientific efficacy
  • Explores functional problems with use in medical settings

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Virtual reality in psychotherapy: Review. CyberPsychology & Behavior, 8(3), 220À230. Rizzo, A. , Buckwalter, J. , & Neumann, U. (1997). Virtual reality and cognitive rehabilitation: A brief review of the future. The Journal of Head Trauma Rehabilitation, 12(6), 1À15. Rizzo, A. , Buckwalter, J. , et al. (2011). An intelligent virtual human system for providing healthcare information and support. Studies in Health Technology and Informatics, 163, 503À509. Rogers, C. (1959) A theory of therapy, personality and interpersonal relationships as developed in the client-centered framework.

How they integrate information into their decision-making over time (Patel, Kaufman, & Arocha, 2002)). This represents a major obstacle, because currently much of the focus of AI and clinical decision support systems (CDSS) in healthcare is on making a single recommendation at a single timepoint. But that is not how health care really works. This chapter is laid out as follows. “The History À Expert Systems and Clinical Artificial Intelligence in Health Care” provides a brief history of AI, expert systems, and CDSS in physical and mental healthcare.

We return to this point in the “Conclusion” section. SUMMARY OF DYNAMICAL APPROACHES FOR CLINICAL AI The main point of this section is that temporal modeling and/or dynamical approaches to clinical AI better fit with the way we think about the world, and the challenges we face in decision-making. They leverage the information inherent in the dynamical process of perception (observation) and action (treatment). Recent research has shown empirically that such approaches can outperform current models of health care, providing a nearly 50% increase in outcomes for roughly half the costs (Bennett & Hauser, 2013).

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