Background: Placebo and nocebo effects occur in clinical or laboratory medical contexts after administration of an inert treatment or as part of active treatments and are due to psychobiological mechanisms such as expectancies of the patient. Placebo and nocebo studies have evolved from predominantly methodological research into a far-reaching interdisciplinary field that is unravelling the neurobiological, behavioural and clinical underpinnings of these phenomena in a broad variety of medical conditions. As a consequence, there is an increasing demand from health professionals to develop expert recommendations about evidence-based and ethical use of placebo and nocebo effects for clinical practice. Methods: A survey and interdisciplinary expert meeting by invitation was organized as part of the 1st Society for Interdisciplinary Placebo Studies (SIPS) conference in 2017. Twenty-nine internationally recognized placebo researchers participated. Results: There was consensus that maximizing placebo effects and minimizing nocebo effects should lead to better treatment outcomes with fewer side effects. Experts particularly agreed on the importance of informing patients about placebo and nocebo effects and training health professionals in patient-clinician communication to maximize placebo and minimize nocebo effects. Conclusions: The current paper forms a first step towards developing evidence-based and ethical recommendations about the implications of placebo and nocebo research for medical practice, based on the current state of evidence and the consensus of experts. Future research might focus on how to implement these recommendations, including how to optimize conditions for educating patients about placebo and nocebo effects and providing training for the implementation in clinical practice.

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Background The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics and related fields. Objective This study aimed to explore general practitioners’ (GPs’) opinions about the potential impact of future technology on key tasks in primary care. Methods In June 2018, we conducted a Web-based survey of 720 UK GPs’ opinions about the likelihood of future technology to fully replace GPs in performing 6 key primary care tasks, and, if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. This study involved qualitative descriptive analysis of written responses (“comments”) to an open-ended question in the survey. Results Comments were classified into 3 major categories in relation to primary care: (1) limitations of future technology, (2) potential benefits of future technology, and (3) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning and the ability to provide value-based care as necessitating physicians’ judgments. Perceived benefits of technology included expectations about improved efficiencies, in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians. Conclusions This study presents timely information on physicians’ views about the scope of artificial intelligence (AI) in primary care. Overwhelmingly, GPs considered the potential of AI to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs’ views.

Background:Futurists have predicted that new autonomous technologies, embedded with artificial intelligence (AI) and machine learning (ML), will lead to substantial job losses in many sectors disrupting many aspects of healthcare. Mental health appears ripe for such disruption given the global illness burden, stigma, and shortage of care providers.Objective: To characterize the global psychiatrist community's opinion regarding the potential of future autonomous technology (referred to here as AI/ML) to replace key tasks carried out in mental health practice.Design: Cross sectional, random stratified sample of psychiatrists registered with Sermo, a global networking platform open to verified and licensed physicians. Main outcome measures:We measured opinions about the likelihood that AI/ML tools would be able to fully replace -not just assist -the average psychiatrist in performing 10 key psychiatric tasks. Among those who considered replacement likely, we measured opinions about how many years from now such a capacity might emerge. We also measured psychiatrist's perceptions about whether benefits of AI/ML would outweigh the risks.Results: Survey respondents were 791 psychiatrists from 22 countries representing North America, South America, Europe and Asia-Pacific. Only 3.8% of respondents felt it was likely that future technology would make their jobs obsolete and only 17% felt that future AI/ML was likely to replace a human clinician for providing empathetic care. Documenting and updating medical records (75%) and synthesizing information (54%) were the two tasks where a majority predicted that AI/ML could fully replace human psychiatrists. Female-and US-based doctors were more uncertain that the benefits of AI would outweigh risks than male-and non-US doctors, respectively. Around one in 2 psychiatrists did however predict that their jobs would be substantially changed by AI/ML. Conclusions:To our knowledge, this is the first global survey to seek the opinions of physicians on the impact of autonomous AI/ML on the future of psychiatry. Our findings provide compelling insights into how physicians think about AI/ML which in turn may help us better integrate technology and reskill doctors to enhance mental health care.

Psychologists (and subsequently the media) have defined ‘Facebook depression’ as the affective result of spending too much time on the social networking site (Selfhout et al., 2009; Kross et al., 2013). Some social psychologists have denied that Facebook is causally implicated in any such negative affect (Jelenchick et al., 2013). This article argues that if we want to understand modern mass media and new social media, we need a better understanding of the (old) psychology bequeathed us by natural selection (Barkow et al., 2012). Disentangling the relationship between social media and depression using evolutionary social competition theories of depression, I argue that the mismatch between current social milieu and the environment of evolutionary adaption affords some predictions about the use of social media as a trigger for mild depression or dysphoria. I hypothesize that users of Facebook may be more susceptible to causal triggers for mild depression under the following (specific) circumstances: (a) the greater the number of ‘friends’ that the user has online; (b) the greater the time that the user spends reading updates from this wide pool of friends; (c) the user does so regularly; and (d) the content of the updates tends to a bragging nature. I hypothesize that the frequency and the number of displays of higher status cues observed by the user may incur the perception of low relative social value among users (automatically triggering this response). The article concludes with directions for future research on the behavioral and cognitive effects of social media sites such as Facebook.

Background The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. Objective This study aimed to explore psychiatrists’ opinions about the potential impact innovations in artificial intelligence and machine learning on psychiatric practice Methods In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written responses (“comments”) to three open-ended questions in the survey. Results Comments were classified into four major categories in relation to the impact of future technology on: (1) patient-psychiatrist interactions; (2) the quality of patient medical care; (3) the profession of psychiatry; and (4) health systems. Overwhelmingly, psychiatrists were skeptical that technology could replace human empathy. Many predicted that ‘man and machine’ would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. Conclusions This study presents timely information on psychiatrists’ views about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

<b><i>Introduction:</i></b> Clinical and laboratory studies demonstrate that placebo and nocebo effects influence various symptoms and conditions after the administration of both inert and active treatments. <b><i>Objective:</i></b> There is an increasing need for up-to-date recommendations on how to inform patients about placebo and nocebo effects in clinical practice and train clinicians how to disclose this information. <b><i>Methods:</i></b> Based on previous clinical recommendations concerning placebo and nocebo effects, a 3-step, invitation-only Delphi study was conducted among an interdisciplinary group of internationally recognized experts. The study consisted of open- and closed-ended survey questions followed by a final expert meeting. The surveys were subdivided into 3 parts: (1) informing patients about placebo effects, (2) informing patients about nocebo effects, and (3) training clinicians how to communicate this information to the patients. <b><i>Results:</i></b> There was consensus that communicating general information about placebo and nocebo effects to patients (e.g., explaining their role in treatment) could be beneficial, but that such information needs to be adjusted to match the specific clinical context (e.g., condition and treatment). Experts also agreed that training clinicians to communicate about placebo and nocebo effects should be a regular and integrated part of medical education that makes use of multiple formats, including face-to-face and online modalities. <b><i>Conclusions:</i></b> The current 3-step Delphi study provides consensus-based recommendations and practical considerations for disclosures about placebo and nocebo effects in clinical practice. Future research is needed on how to optimally tailor information to specific clinical conditions and patients’ needs, and on developing standardized disclosure training modules for clinicians.

National surveys of primary care physicians demonstrate that placebo use is prevalent. Against their widespread use, until recently, it was assumed among researchers that placebos must be deceptively prescribed for beneficial effects to be elicited. However, a new programme of research in placebo studies indicates that it may be possible to harness placebo effects in clinical practice via ethical, non-deceptively prescribed ‘open label placebos’ (‘OLPs’). To date, there have been 14 small scale clinical and experimental trials into OLPs. Results suggest therapeutic potential of these treatments for a range of conditions and symptoms. In this evidence-based Analysis we identify conceptual issues that, if not given due consideration, risk undermining research methodologies in OLP trials. Counterintuitively, owing to the nuances posed by placebo terminology, and the difficulties of designing placebos controls in OLP trials, we suggest that experimentalists reflect more deeply when formulating adequate comparison groups. Further research is needed to disentangle which specific components of OLPs are effective, such as: the rationale provided to participants; the quality of provider interaction; and/or the action of taking the pills. We conclude with recommendations for how researchers might take up the significant challenge of devising optimal placebo controls for OLP clinical trials. Although these issues are intricate, they are not merely academic: without due diligence to conceptual, and as a consequence, methodological considerations, OLP effect sizes may be over- or underestimated. We conclude that there may yet be potential to use OLPs in medical practice but clinical translation depends on rigorously controlled research.