Automated voice calls with health information are a proven method for disseminating maternal and child health information among beneficiaries and are deployed in several programs around the world. However, these programs often suffer from beneficiary dropoffs and poor engagement. In previous work, through real-world trials, we showed that an AI model, specifically a restless bandit model, could identify beneficiaries who would benefit most from live service call interventions, preventing dropoffs and boosting engagement. However, one key question has remained open so far: does such improved listenership via AI-targeted interventions translate into beneficiaries' improved knowledge and health behaviors? We present a first study that shows not only listenership improvements due to AI interventions, but also simultaneously links these improvements to health behavior changes. Specifically, we demonstrate that AI-scheduled interventions, which enhance listenership, lead to statistically significant improvements in beneficiaries' health behaviors such as taking iron or calcium supplements in the postnatal period, as well as understanding of critical health topics during pregnancy and infancy. This underscores the potential of AI to drive meaningful improvements in maternal and child health.

Flow theory describes an optimal cognitive state where individuals experience deep focus and intrinsic motivation when a task's difficulty aligns with their skill level. In AI-augmented reasoning, interventions that disrupt the state of cognitive flow can hinder rather than enhance decision-making. This paper proposes a context-aware cognitive augmentation framework that adapts interventions based on three key contextual factors: type, timing, and scale. By leveraging multimodal behavioral cues (e.g., gaze behavior, typing hesitation, interaction speed), AI can dynamically adjust cognitive support to maintain or restore flow. We introduce the concept of cognitive flow, an extension of flow theory in AI-augmented reasoning, where interventions are personalized, adaptive, and minimally intrusive. By shifting from static interventions to context-aware augmentation, our approach ensures that AI systems support deep engagement in complex decision-making and reasoning without disrupting cognitive immersion.

Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification. Here, an artificial intelligence (AI) agent can provide personalized interventions to help individuals stick to their goals. In these settings, the AI agent must personalize rapidly (before the individual disengages) and interpretably, to help us understand the behavioral interventions. In this paper, we introduce Behavior Model Reinforcement Learning (BMRL), a framework in which an AI agent intervenes on the parameters of a Markov Decision Process (MDP) belonging to a boundedly rational human agent. Our formulation of the human decision-maker as a planning agent allows us to attribute undesirable human policies (ones that do not lead to the goal) to their maladapted MDP parameters, such as an extremely low discount factor. Furthermore, we propose a class of tractable human models that captures fundamental behaviors in frictionful tasks. Introducing a notion of MDP equivalence specific to BMRL, we theoretically and empirically show that AI planning with our human models can lead to helpful policies on a wide range of more complex, ground-truth humans.

AI-driven keyword research has become indispensable for bloggers looking to grow their audience and boost their online presence. By leveraging advanced ML algorithms, AI tools provide data-driven insights into user search behavior, revealing high-potential keywords to target. This process helps you create compelling, relevant, and searchable content that attracts organic traffic and improves your blog's search engine ranking. In this article, we'll introduce you to the benefits of AI keyword research, the best tools to use in 2023, and actionable tips for harnessing their potential to grow your blog exponentially. AI keyword research is the process of using machine learning algorithms and advanced data analytics to identify high-potential keywords that can help improve a website's search engine ranking and drive traffic to the site.

Mexican photographer and architect Eetov explores an alternate architectural reality in New York, interweaving photographs of iconic structures with AI interventions. Titled'AI Buildings' the series explores the application of artificial intelligence design tool DALL·E 2 through hybridization with images of streetscapes and architecture across the city. Through resulting compositions of familiar urban icons reimagined in new expressions, the project investigates how bustling metropolises like New York could have been adopted if, for one reason or another, they had not been designed as we know them today. 'AI buildings' ponders the constant human fascination for the speculation of the imagined and its encompassing fantasy. As a result, the series reveals photographs of reality becoming intervened through propositions whose main premise is the variations of specific elements of the image -- in this case, the building itself.

Dr. Rubeta Matin, NHS Consultant Dermatologist, reveals the challenges of setting up a new national skin database to support the development of dermatological AI in the UK It's common knowledge that the chances of survival increase dramatically if melanoma is detected and treated early. However, many algorithm-based applications that claim to identify potentially dangerous-looking pigment on the skin have not been formally and appropriately validated in intervention studies. There are also not many systematic and rigorous reviews to discover the true accuracy of these skin cancer diagnosing algorithms, especially those that were tested in an artificial research setting that may not be representative of the real world. It's reasons like these that drive dermatologists to question whether the false assurance given by these applications may delay individuals from seeking medical advice. Last February, a new study published in the BMJ revealed mobile applications that assess the risks of suspicious moles may not be reliable enough to detect all forms of skin cancer.

As previously mentioned, we employed a 2x2 design, in which we studied two news conditions (familiar news and novel news) and two AI conditions (No AI and AI Intervention). The familiar news condition contained news articles on vaccination and climate change, both of which had been widely reported prior to this study, while the novel news condition contained news articles on COVID-19. The No AI condition provided just the article, while the AI Intervention condition displayed one of two statements at the top of the article: either "Our smart AI system rates this article as accurate and reliable" or "Our smart AI system rates this article as inaccurate and unreliable." Each participant read one randomly chosen article and answered two questions: 1. Do you believe the information in this news article?

Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when reporting a study. The use of AI reporting guidelines that address potential sources of bias specific to studies involving AI interventions has the potential to improve the quality of AI studies, through improvements in their design and delivery, and the completeness and transparency of their reporting. With a number of guidance documents relating to AI studies emerging from different specialist societies, this Review article provides researchers with some key principles for selecting the most appropriate reporting guidelines for a study involving an AI intervention. As the main determinants of a high‐quality study are contained within the methodology of the study design rather than the intervention, researchers are recommended to use reporting guidelines that are specific to the study design, and then supplement them with AI‐specific guidance contained within available AI reporting guidelines.

In case anyone missed it: attention on AI's application to healthcare is apparently at'peak hype'. With the volume of healthcare data doubling every 2 to 5 years, it is no surprise that many are using AI to make sense of such vast amounts of data, and development of medical AI technologies is progressing rapidly. At the same time, the COVID-19 pandemic has exposed vulnerabilities in healthcare systems around the world, highlighting the need for technological interventions in healthcare. In line with these trends, the healthcare AI market is expected to grow from US$2 billion in 2018 to US$36 billion by 2025. The breadth of AI's application in healthcare is impressive, ranging from diagnostic chat bots to AI robot-assisted surgery.

In the past six months, COVID-19 has evolved from a speck on the world radar to a full-blown pandemic. While it has claimed the lives of many and shed a massive spotlight on some of the major issues in healthcare, it has also served as a catalyst for innovation. As with nearly every element of the healthcare system, applications of machine learning and artificial intelligence (AI) have also been transformed by the pandemic. Although the power of machine learning and AI was being put to significant use prior to the Coronavirus outbreak, there is now increased pressure to understand the underlying patterns to help us prepare for any epidemic that might hit the world in the future. How have AI interventions fared so far?