Is combined antidepressant medication (ADM) and psychotherapy better than either monotherapy at preventing suicide attempts and other psychiatric serious adverse events for depressed patients? A rare events meta-analysis

Zainal N. H. (2024).

Psychological medicine, 54(3), 457–472.

https://doi.org/10.1017/S0033291723003306

Abstract

Antidepressant medication (ADM)-only, psychotherapy-only, and their combination are the first-line treatment options for major depressive disorder (MDD). Previous meta-analyses of randomized controlled trials (RCTs) established that psychotherapy and combined treatment were superior to ADM-only for MDD treatment remission or response. The current meta-analysis extended previous ones by determining the comparative efficacy of ADM-only, psychotherapy-only, and combined treatment on suicide attempts and other serious psychiatric adverse events (i.e. psychiatric emergency department [ED] visit, psychiatric hospitalization, and/or suicide death; SAEs). Peto odds ratios (ORs) and their 95% confidence intervals were computed from the present random-effects meta-analysis. Thirty-four relevant RCTs were included. Psychotherapy-only was stronger than combined treatment (1.9% v. 3.7%; OR 1.96 [1.20-3.20], p = 0.012) and ADM-only (3.0% v. 5.6%; OR 0.45 [0.30-0.67], p = 0.001) in decreasing the likelihood of SAEs in the primary and trim-and-fill sensitivity analyses. Combined treatment was better than ADM-only in reducing the probability of SAEs (6.0% v. 8.7%; OR 0.74 [0.56-0.96], p = 0.029), but this comparative efficacy finding was non-significant in the sensitivity analyses. Subgroup analyses revealed the advantage of psychotherapy-only over combined treatment and ADM-only for reducing SAE risk among children and adolescents and the benefit of combined treatment over ADM-only among adults. Overall, psychotherapy and combined treatment outperformed ADM-only in reducing the likelihood of SAEs, perhaps by conferring strategies to enhance reasons for living. Plausibly, psychotherapy should be prioritized for high-risk youths and combined treatment for high-risk adults with MDD.

Here are some thoughts:

This meta-analysis examines the comparative efficacy of antidepressant medication (ADM), psychotherapy, and combined treatment in preventing suicide attempts and other serious psychiatric adverse events (SAEs) among patients with major depressive disorder (MDD). The study found that psychotherapy-only was more effective than both combined treatment and ADM-only in reducing the likelihood of SAEs. Combined treatment showed better outcomes than ADM-only in reducing SAE probability, though this finding was not significant in sensitivity analyses.

Age-specific effects were observed, with psychotherapy-only outperforming both combined treatment and ADM-only in reducing SAE risk for children and adolescents, while combined treatment was more beneficial than ADM-only for adults. These findings suggest that psychotherapy should be prioritized for high-risk youth with MDD, while combined treatment may be more beneficial for high-risk adults.

The study reinforces the importance of psychotherapy in MDD treatment, particularly for reducing serious adverse events. It also indicates that ADM-only may be less effective in preventing SAEs compared to treatments that include psychotherapy. These findings provide valuable insights for tailoring treatment approaches for MDD patients, emphasizing the critical role of psychotherapy in preventing serious adverse events and potentially saving lives.

Ethical debates amidst flawed healthcare artificial intelligence metrics

Gallifant, J., et al. (2024).

Npj Digital Medicine, 7(1).

https://doi.org/10.1038/s41746-024-01242-1

Healthcare AI faces an ethical dilemma between selective and equitable deployment, exacerbated by flawed performance metrics. These metrics inadequately capture real-world complexities and biases, leading to premature assertions of effectiveness. Improved evaluation practices, including continuous monitoring and silent evaluation periods, are crucial. To address these fundamental shortcomings, a paradigm shift in AI assessment is needed, prioritizing actual patient outcomes over conventional benchmarking.

Artificial intelligence (AI) is poised to bridge the deployment gap with increasing capabilities for remote patient monitoring, handling of diverse time series datasets, and progression toward the promise of precision medicine. This proximity also underscores the urgency of confronting the translational risks accompanying this technological evolution and maximizing alignment with fundamental principles of ethical, equitable, and effective deployment. The recent work by Goetz et al. surfaces a critical issue at the intersection of technology and healthcare ethics: the challenge of generalization and fairness in health AI applications1. This is a complex issue where equal performance across subgroups can be at odds with overall performance metrics2.

Specifically, it highlights one potential avenue to navigate variation in model performance among subgroups based on the concept of “selective deployment”3. This strategy asserts that limiting the deployment of the technology to the subgroup in which it works well facilitates benefits for those subpopulations. The alternative is not to deploy the technology in the optimal performance group but instead adopt a standard of equity in the performance overall to achieve parity among subgroups, what might be termed “equitable deployment”. Some view this as a requirement to “level down” performance for the sake of equity, a view that is not unique to AI or healthcare and is the subject of a broader ethical debate4,5,6. Proponents of equitable deployment would counter: Can a commitment to fairness justify not deploying a technology that is likely to be effective but only for a specific subpopulation?

The info is here.

Here are some thoughts:

The article explores the intricate ethical dilemmas surrounding the deployment of AI in healthcare, particularly the tension between selective and equitable deployment. Selective deployment involves using AI in specific cases where it performs best, potentially maximizing benefits for those groups but risking health disparities for others. Equitable deployment, on the other hand, seeks to ensure fairness across all patient groups, which might require accepting lower performance in certain areas to avoid exacerbating inequalities. The challenge lies in balancing these approaches, as what is effective for one group may not be so for another.

Flawed performance metrics are highlighted as a significant issue, as they may not capture real-world complexities and biases. This can lead to premature assertions of AI effectiveness, where systems are deployed based on metrics that look good in tests but fail in practical settings. The article emphasizes the need for improved evaluation practices, such as continuous monitoring and silent evaluation periods, to ensure AI systems perform well in diverse and dynamic healthcare environments.

A paradigm shift is called for, prioritizing actual patient outcomes over conventional benchmarking. This approach recognizes that patient care is influenced by numerous factors beyond just AI performance. The potential of AI to bridge the deployment gap, through capabilities like remote patient monitoring and precision medicine, is exciting but also underscores the need for caution in addressing ethical risks.

Generalization and fairness in AI applications are critical, as ensuring effectiveness across different subgroups is challenging. The concept of selective deployment, while beneficial for specific groups, could disadvantage others. Equitable deployment, aiming for parity among subgroups, may require balancing effectiveness and equality, a complex task influenced by social and political factors in healthcare.

The article underscores the importance of addressing "bias exhaust," or residual biases in AI models stemming from systemic healthcare issues, to develop fair AI systems. Distinguishing between acceptable variability in medical conditions and impermissible bias is essential, as is continuous evaluation to monitor AI performance in real-world settings.

Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education

Dunlap, P. a. B., & Michalowski, M. (2024).

JMIR Nursing, 7, e62678.

https://doi.org/10.2196/62678

Abstract

The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of health care and subsequently in nursing. This viewpoint explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine-learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including 4 recommendations for future directions in nursing practice, research, and education and 2 hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality and relevant data for machine learning applications.

Here are some thoughts:

The article explores integrating AI in nursing, focusing on ethical considerations vital to patient trust and care quality. It identifies risks like bias, data privacy issues, and the erosion of human-centered care. The paper argues for interdisciplinary frameworks and education to help nurses navigate these challenges. Ethics ensure AI aligns with professional values, safeguarding equity, autonomy, and informed decision-making. With thoughtful integration, AI can empower nursing while upholding ethical standards.

Biology is not ethics: A response to Jerry Coyne's anti-trans essay

Aaron Rabinowitz

Friendly Atheist

Originally posted 2 JAN 25

The Freedom From Religion Foundation recently faced criticism for posting and then removing an editorial by Jerry Coyne entitled “Biology is Not Bigotry,” which he wrote in response to an FFRF article by Kat Grant entitled “What is a Woman?” In his piece, Coyne used specious reasoning and flawed research to argue that transgender individuals are more likely to be sexual predators than cisgender individuals and that they should therefore be barred from some jobs and female-only spaces.

As an ethicist I’m not here to argue biology. I don’t know what the right approach is to balancing phenotypic and genotypic accounts of sex. Luckily, despite Coyne’s framing of the controversy, Coyne is also not here to argue biology. He’s here to argue ethics, and his ethics regarding trans issues consist of bigoted claims leading to discriminatory conclusions.

By making ethics claims like “transgender women… should not serve as rape counselors and workers in battered women’s shelters,” while pretending to only be arguing about biological definitions, Coyne effectively conflates biology with ethics. By conflating biology and ethics, Coyne seeks to transfer perceptions of his expertise from one to the other, so that his claims in both domains are treated with deference, rather than challenged as ill-formed and harmful. Biology is not bigotry, but conflating biology with ethics is one of the most common ways to end up doing a bigotry. Historically, that’s how you slide from genetics to genocide.

The article is here.

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In this essay, Rabinowitz critiques Coyne's conflation of biological arguments with ethical judgments concerning transgender individuals. Rabinowitz contends that Coyne's assertions—such as barring transgender women from roles like rape counselors or access to female-only spaces—are ethically unsound and stem from misinterpreted data. He emphasizes that ethical decisions should not be solely based on biological considerations and warns against using flawed research to justify discriminatory practices.

Rabinowitz highlights that Coyne's approach exemplifies how misapplying biological concepts to ethical discussions can lead to bigotry and discrimination. He argues that such reasoning has historically been used to marginalize groups by labeling them as morally deficient based on misinterpreted or selective data. Rabinowitz calls for a clear distinction between biological facts and ethical values, advocating for inclusive and non-discriminatory practices that respect human rights.

This critique underscores the importance of separating scientific observations from ethical prescriptions, cautioning against the misuse of biology to justify exclusionary or harmful policies toward marginalized communities.

Autonomous Alignment with Human Value on Altruism through Considerate Self-imagination and Theory of Mind

Tong, H., Lum, E., et al. (2024, December 31).

arXiv.org.

https://arxiv.org/abs/2501.00320

Abstract

With the widespread application of Artificial Intelligence (AI) in human society, enabling AI to autonomously align with human values has become a pressing issue to ensure its sustainable development and benefit to humanity. One of the most important aspects of aligning with human values is the necessity for agents to autonomously make altruistic, safe, and ethical decisions, considering and caring for human well-being. Current AI extremely pursues absolute superiority in certain tasks, remaining indifferent to the surrounding environment and other agents, which has led to numerous safety risks. Altruistic behavior in human society originates from humans’ capacity for empathizing others, known as Theory of Mind (ToM), combined with predictive imaginative interactions before taking action to produce thoughtful and altruistic behaviors. Inspired by this, we are committed to endow agents with considerate self-imagination and ToM capabilities, driving them through implicit intrinsic motivations to autonomously align with human altruistic values. By integrating ToM within the imaginative space, agents keep an eye on the well-being of other agents in real time, proactively anticipate potential risks to themselves and others, and make thoughtful altruistic decisions that balance negative effects on the environment. The ancient Chinese story of Sima Guang Smashes the Vat illustrates the moral behavior of the young Sima Guang smashed a vat to save a child who had accidentally fallen into it, which is an excellent reference scenario for this paper. We design an experimental scenario similar to Sima Guang Smashes the Vat and its variants with different complexities, which reflects the trade-offs and comprehensive considerations between self-goals, altruistic rescue, and avoiding negative side effects.

The article is here.

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As artificial intelligence (AI) becomes increasingly integrated into our daily lives, ensuring that these systems align with human values has become a pressing challenge. One critical aspect of this alignment is equipping AI with the ability to make decisions that reflect altruism, safety, and ethical principles. A recent study titled *Autonomous Alignment with Human Value on Altruism through Considerate Self-Imagination and Theory of Mind* explores innovative methods to address this challenge.

Current AI systems often prioritize efficiency and task completion at the expense of broader ethical considerations, such as the potential harm to humans or the environment. This narrow focus has led to safety risks and unintended consequences, highlighting the urgent need for AI to autonomously align with human values. The researchers propose a solution inspired by human cognitive abilities, particularly Theory of Mind (ToM)—our capacity to empathize with others—and self-imagination. By integrating these capabilities into AI, agents can predict the effects of their actions on others and the environment, enabling them to make altruistic and ethical decisions.

The researchers drew inspiration from the ancient Chinese story of *Sima Guang Smashes the Vat*, where a young boy prioritizes saving a child over preserving a water vat. This story exemplifies the moral trade-offs inherent in decision-making. Similarly, the study designed experimental environments where AI agents faced conflicting goals, such as balancing self-interest, altruistic rescue, and environmental preservation. The results demonstrated that agents equipped with the proposed framework could prioritize rescuing others while minimizing environmental damage and achieving their objectives.

The core of the framework lies in three components. First, the *self-imagination module* enables agents to simulate the potential consequences of their actions using random reward functions based on past experiences. Second, agents learn to avoid negative side effects by evaluating potential harm using baseline comparisons. Finally, through ToM, agents assess the impact of their actions on others by estimating the value of others’ states, fostering empathy and a deeper understanding of their needs. Together, these mechanisms allow AI systems to generate intrinsic motivations to act altruistically without relying solely on external rewards.

To validate their approach, the researchers compared their framework with traditional AI models and empathy-focused methods. Their framework outperformed others in achieving ethical and safe outcomes across various scenarios. Notably, the agents displayed robust decision-making abilities even when tested under different configurations and network architectures, demonstrating the generalizability of the approach.

This research represents a significant step toward creating AI systems that are not only intelligent but also moral and ethical. While the experimental environments were simplified, they lay the groundwork for developing more complex models capable of navigating real-world ethical dilemmas. Future research aims to expand these scenarios and incorporate advanced tools like large language models to deepen AI’s understanding of human morality.

Aligning AI with human altruistic values is not just a technical challenge but a moral imperative. By embedding empathy and self-imagination into AI, we move closer to a future where machines can contribute positively to society, safeguarding humanity and the environment. This study inspires us to rethink AI’s potential, not merely as a tool but as a collaborative partner in building a safer and more compassionate world.

Augmenting research consent: Should large language models (LLMs) be used for informed consent to clinical research?

Allen, J. W., et al. (2024).

Research Ethics, in press.

Abstract

The integration of artificial intelligence (AI), particularly large language models (LLMs) like OpenAI’s ChatGPT, into clinical research could significantly enhance the informed consent process. This paper critically examines the ethical implications of employing LLMs to facilitate consent in clinical research. LLMs could offer considerable benefits, such as improving participant understanding and engagement, broadening participants’ access to the relevant information for informed consent, and increasing the efficiency of consent procedures. However, these theoretical advantages are accompanied by ethical risks, including the potential for misinformation, coercion, and challenges in accountability. Given the complex nature of consent in clinical research, which involves both written documentation (in the form of participant information sheets and informed consent forms) and in-person conversations with a researcher, the use of LLMs raises significant concerns about the adequacy of existing regulatory frameworks. Institutional Review Boards (IRBs) will need to consider substantial reforms to accommodate the integration of LLM-based consent processes. We explore five potential models for LLM implementation, ranging from supplementary roles to complete replacements of current consent processes, and offer recommendations for researchers and IRBs to navigate the ethical landscape. Thus, we aim to provide practical recommendations to facilitate the ethical introduction of LLM-based consent in research settings by considering factors such as participant understanding, information accuracy, human oversight and types of LLM applications in clinical research consent.

A pdf is here.

Here are some thoughts:

This paper examines the ethical implications of using large language models (LLMs) for informed consent in clinical research. While LLMs offer potential benefits, including personalized information, increased participant engagement, and improved efficiency, they also present risks related to accuracy, manipulation, and accountability. The authors explore five potential models for LLM implementation in consent processes, ranging from supplementary roles to complete replacements of current methods. Ultimately, they propose a hybrid approach that combines traditional consent methods with LLM-based interactions to maximize participant autonomy while maintaining ethical safeguards.

Creating ‘Mirror Life’ Could Be Disastrous, Scientists Warn

Simon Makin

Scientific American

Originally posted 14 DEC 24

A category of synthetic organisms dubbed “mirror life,” whose component molecules are mirror images of their natural counterpart, could pose unprecedented risks to human life and ecosystems, according to a perspective article by leading experts, including Nobel Prize winners. The article, published in Science on December 12, is accompanied by a lengthy report detailing their concerns.

Mirror life has to do with the ubiquitous phenomenon in the natural world in which a molecule or another object cannot simply be superimposed on another. For example, your left hand can’t simply be turned over to match your right hand. This handedness is encountered throughout the natural world.

Groups of molecules of the same type tend to have the same handedness. The nucleotides that make up DNA are nearly always right-handed, for instance, while proteins are composed of left-handed amino acids.

Handedness, more formally known as chirality, is hugely important in biology because interactions between biomolecules rely on them having the expected form. For example, if a protein’s handedness is reversed, it cannot interact with partner molecules, such as receptors on cells. “Think of it like hands in gloves,” says Katarzyna Adamala, a synthetic biologist at the University of Minnesota and a co-author of the article and the accompanying technical report, which is almost 300 pages long. “My left glove won’t fit my right hand.”

The article is here.

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Oh great, another existential risk.

Scientists are sounding the alarm about the potential risks of creating "mirror life," synthetic biological systems with mirrored molecular structures. Researchers have long explored mirror life's possibilities in medicine, biotechnology and other fields. However, experts now warn that unleashing these synthetic organisms could have disastrous consequences.

Mirror life forms may interact unpredictably with natural organisms, disrupting ecosystems and causing irreparable damage. Furthermore, synthetic systems could inadvertently amplify harmful pathogens or toxins, posing significant threats to human health. Another concern is uncontrolled evolution, where mirror life could mutate and spread uncontrollably. Additionally, synthetic organisms may resist decomposition, persisting in environments and potentially causing long-term harm.

To mitigate these risks, scientists advocate a precautionary approach, emphasizing cautious research and regulation. Thorough risk assessments must be conducted before releasing mirror life into the environment. Researchers also stress the need for containment strategies to prevent unintended spread. By taking a cautious stance, scientists hope to prevent potential catastrophes.

Mirror life research aims to revolutionize various fields, including medicine and biotechnology. However, experts urge careful consideration to avoid unforeseen consequences. As science continues to advance, addressing these concerns will be crucial in ensuring responsible development and minimizing risks associated with mirror life.

Advancements in AI-driven Healthcare: A Comprehensive Review of Diagnostics, Treatment, and Patient Care Integration

Kasula, B. Y. (2024, January 18).

International Journal of Machine Learning

for Sustainable Development.

Volume 6 (1).

Abstract

This research paper presents a comprehensive review of the recent advancements in AI-

driven healthcare, focusing on diagnostics, treatment, and the integration of AI technologies in

patient care. The study explores the evolution of artificial intelligence applications in medical

imaging, diagnosis accuracy, personalized treatment plans, and the overall enhancement of

healthcare delivery. Ethical considerations and challenges associated with AI adoption in

healthcare are also discussed. The paper concludes with insights into the potential future

developments and the transformative impact of AI on the healthcare landscape.

A pdf of the research is here.

Here are some thoughts:

This research paper provides a comprehensive review of recent advancements in AI-driven healthcare, focusing on diagnostics, treatment, and the integration of AI technologies in patient care. The study explores the evolution of artificial intelligence applications in medical imaging, diagnosis accuracy, personalized treatment plans, and the overall enhancement of healthcare delivery. It discusses the transformative impact of AI on healthcare, highlighting key achievements, challenges, and ethical considerations associated with its widespread adoption.

The paper examines AI's role in improving diagnostic accuracy, particularly in medical imaging, and its contribution to developing personalized treatment plans. It also addresses the ethical dimensions of AI in healthcare, including patient privacy, data security, and equitable distribution of AI-driven healthcare benefits. The research emphasizes the need for a holistic approach to AI integration in healthcare, calling for collaboration between healthcare professionals, technologists, and policymakers to navigate the evolving landscape successfully.

It is important for psychologists to understand the content of this article for several reasons. Firstly, AI is increasingly being applied in mental health diagnosis and treatment, as mentioned in the paper's references. Psychologists need to be aware of these advancements to stay current in their field and potentially incorporate AI-driven tools into their practice. Secondly, the ethical considerations discussed in the paper, such as patient privacy and data security, are equally relevant to psychological practice. Understanding these issues can help psychologists navigate the ethical challenges that may arise with the integration of AI in mental health care.

Moreover, the paper's emphasis on personalized medicine and treatment plans is particularly relevant to psychology, where individualized approaches are often crucial. By understanding AI's potential in this area, psychologists can explore ways to enhance their treatment strategies and improve patient outcomes. Lastly, as healthcare becomes increasingly interdisciplinary, psychologists need to be aware of technological advancements in other medical fields to collaborate effectively with other healthcare professionals and provide comprehensive care to their patients.

AI has an environmental problem.

Here’s what the world can do about that.

UN Environment Programme

Originally posted 21 Sept 24

There are high hopes that artificial intelligence (AI) can help tackle some of the world’s biggest environmental emergencies. Among other things, the technology is already being used to map the destructive dredging of sand and chart emissions of methane, a potent greenhouse gas.  

But when it comes to the environment, there is a negative side to the explosion of AI and its associated infrastructure, according to a growing body of research. The proliferating data centres that house AI servers produce electronic waste. They are large consumers of water, which is becoming scarce in many places. They rely on critical minerals and rare elements, which are often mined unsustainably. And they use massive amounts of electricity, spurring the emission of planet-warming greenhouse gases.  

“There is still much we don’t know about the environmental impact of AI but some of the data we do have is concerning,” said Golestan (Sally) Radwan, the Chief Digital Officer of the United Nations Environment Programme (UNEP). “We need to make sure the net effect of AI on the planet is positive before we deploy the technology at scale.”  

This week, UNEP released an issue note that explores AI’s environmental footprint and considers how the technology can be rolled out sustainably. It follows a major UNEP report, Navigating New Horizons, which also examined AI’s promise and perils. Here’s what those publications found.

The article is here.

Here are some thoughts:

The article discusses the significant environmental impact of artificial intelligence (AI) technologies and proposes solutions to mitigate these effects. AI systems, particularly those requiring substantial computational power, consume vast amounts of energy, often sourced from non-renewable resources, contributing to carbon emissions. Data centers, which host AI operations, also demand considerable energy and water for cooling. Moreover, the production of AI hardware, such as GPUs and servers, involves the extraction of rare earth metals, leading to environmental damage, and the disposal of this hardware contributes to electronic waste.

The article likely suggests several strategies to address these issues, including the development of energy-efficient AI algorithms and hardware, the use of renewable energy sources to power data centers, and the implementation of sustainable practices in hardware production and disposal. It may also advocate for policies that regulate the environmental impact of AI technologies.

Stakeholders, including governments, corporations, and researchers, are probably emphasized as crucial players in creating sustainable AI ecosystems. The importance of public awareness and consumer pressure in driving the industry towards greener practices is likely highlighted as well.

From an ethical standpoint, the article underscores the responsibility of AI developers and companies to minimize environmental harm, balancing technological progress with ecological sustainability. It raises concerns about intergenerational equity, urging sustainable practices to protect the planet for future generations. Corporate accountability is another key ethical consideration, emphasizing the need for tech companies to prioritize environmental sustainability. The role of policy and governance is also stressed, with a call for regulatory frameworks to ensure ethical AI development. Lastly, the article likely emphasizes the moral duty of consumers to demand and be informed about greener AI technologies.