Chatbots are emerging as a promising platform for accessing and delivering healthcare services. The evidence is in the growing number of publicly available chatbots aiming at taking an active role in the provision of prevention, diagnosis, and treatment services. This article takes a closer look at how these emerging chatbots address design aspects relevant to healthcare service provision, emphasizing the Human-AI interaction aspects and the transparency in AI automation and decision making.

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Generative modeling is an artificial intelligence (AI) technique that generates synthetic artifacts by analyzing training examples; learning their patterns and distribution; and then creating realistic facsimiles. Generative AI (GAI) uses generative modeling and advances in deep learning (DL) to produce diverse content at scale by utilizing existing media such as text, graphics, audio, and video. 1,2 While mainly used in research settings, GAI is entering various domains and everyday scenarios. This article sheds light on the unique practical opportunities and challenges GAI brings. GAI TECHNIQUESAlthough there are many forms of GAI, we will look at four of the most common techniques being leveraged today. Generative adversarial networksGenerative adversarial networks (GANs) are the most prevalent GAI technique being used today. 3 A GAN uses a pair of neural networks. One, known as the generator, synthesizes the content (for example, an image of a human face). The second, known as the discriminator, evaluates the authenticity of the generator's content, (that is, whether the face is natural or fake). The networks repeat this generate/discriminate cycle until the generator produces content that the discriminator cannot discern between real and synthetic. Generative Pre-trained TransformerGenerative Pre-trained Transformer (GPT) models generate text in different languages and can create human-sounding words, sentences, and paragraphs on almost any topic and writing style-from convincing news articles and essays to conversations in customer-service chatbots or characters in video games. 4 These have matured over several generations, each with an increased parameter set trained on a more extensive online textual

A normalized difference vegetation index provides local forest managers with much essential annual information about the forest inventory. This article evaluates the possible use of NDVI and CORINE Land Cover databases for better forest management in the municipalities of Kursumlija and Topola in Serbia. The forest areas obtained using CLC were up to 11.5% larger than the official forest area estimates, whereas NDVI gave more precise results. This is of a crucial importance for preventing illegal logging, which is very prevalent in southern Serbian municipalities, which have substantial forested territory. NDVI is very promising for Serbia and also for countries that rarely carry out national forest inventories. This method can also easily be applied to other Balkan countries with a similar situation regarding local forest management.

Background Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590