Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being npj Digital Medicine

Dallas’ Pieces Technologies Gets $2M from National Cancer Institute to Advance Conversational AI for Cancer Patients » Dallas Innovates

You can foun additiona information about ai customer service and artificial intelligence and NLP. “These models can complement human expertise by providing insights beyond traditional visual interpretation and, as we move toward a more integrated, multimodal approach, will reshape the future of medicine.” New healthcare security application templates that can help govern data are also available in public preview in Microsoft Purview, the company said. For example, by consuming and harmonizing national and international SDOH public datasets, healthcare organizations can identify risks and health-related social needs to improve equity in healthcare. “This integration is now enabling organizations to securely access the DAX Copilot conversational data,” including the audio files, draft clinical notes and more, Rustogi said. Consumers also worry that if AI systems generate decisions – such as diagnoses or treatment plans – without human input, it may be unclear who is responsible for errors.

As an example of emerging trends, Cloudera provides “portable cloud-native data analytics.” Cloudera was founded in 2008. Validated at Stanford Medicine, the physician-supervised AI autonomously manages chronic diseases, ChatGPT according to Palo Alto, California-based UpDoc. The company said patient-facing conversational AI can augment physician encounters and could improve access to high-quality affordable care and improve patient outcomes.

Automatic approaches utilize established benchmarks to assess the chatbot’s adherence to specified guidelines, such as using robustness benchmarks alongside metrics like ROUGE or BLEU to evaluate model robustness. Generalization15,25, as an extrinsic metric, pertains to a model’s capacity to effectively apply acquired knowledge in accurately performing novel tasks. In the context of healthcare, the significance of the generalization metric becomes pronounced due to the scarcity of data and information across various medical domains and categories. A chatbot’s ability to generalize enhances its validity in effectively addressing a wide range of medical scenarios. This project will be one of the first rigorous research demonstrations of HITL-based conversational AI in the healthcare domain, the organizations said. Google, for instance, is actively researching how a fine-tuned version of its Gemini model tailored for the medical domain can enhance advanced reasoning.

Software equipped with conversational AI capabilities allows just this, as it understands and mimics human speech. Shield AI is an innovative AI startup that has quickly gained notoriety and capital for its AI pilot technology. Hivemind is an AI pilot that can fly aircraft in both commercial and battle settings, giving users greater insights into their locations and travel paths as well as what’s happening with other pilots and aircraft in their fleet. At this point, Shield AI’s technology is powering several of the vendor’s own intelligent aircraft, including jets, V-BAT teams, and Nova 2. While many large companies offer RPA as part of their overall portfolio—notably SAP, ServiceNow, and IBM—the vendors in this category specialize in creating intelligent automation and RPA solutions to boost productivity.

Experts believe that its ability to analyse patient data and generate personalised treatment plans, as well as assist in interpreting medical images like MRI scans, X-rays, and CT scans, can revolutionise disease diagnosis. According to Singh, the emerging tech is also being used to improve rural healthcare access through telemedicine and remote monitoring, streamline administrative tasks, enhance efficiency and reduce costs to make quality healthcare affordable to all. What we learnt is that while the Indian healthcare industry is strongly positioned to harness the true potential of GenAI, there remains a dire need to get fundamentals like data accuracy, data security, and ethical implementation in place. However, the journey to becoming a distinguishable conversational AI platform was not without its challenges.

Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience.

Extrinsic evaluation metrics

Moreover, the platform is fully transactional and voice-capable, empowered to handle complex conversation scenarios involving multiple queries and commands within a single conversation. This allows MIC to handle elaborate tasks like appointment scheduling, consent capturing, and adherence & service feedback without the need for contact centre agents. All this means we need standards for the AI tools that impact diagnosis and treatment of patients.

Despite this, Chugh is extremely bullish on the growth prospect of companies building small language models (SLMs) working on particular diseases such as obesity or cancer. For instance, Bengaluru-based Healthify has adopted GenAI technology to enhance its chatbot ‘Ria’ and build ‘Snap’ – a photo-to-food recognition system. Singh added that Max is exploring different organisations that can translate patient data into proactive diagnosis, provide tailored treatment plans, and analyse patient segments. All of this had a positive impact on the patient, not just clinically but emotionally, too.

It can integrate every aspect of a digital human into healthcare applications — from speech and translation abilities capable of understanding diverse accents and languages, to realistic animations of facial and body movements. Deloitte’s Frontline AI Teammate, built with NVIDIA AI Enterprise and Deloitte’s Conversational AI Framework, is designed to deliver human-to-machine experiences in healthcare settings. Developed on the NVIDIA Omniverse platform, Deloitte’s lifelike avatar can respond to complex, domain-specific questions that are pivotal in healthcare delivery.

Future research endeavors need to delve deeper into the mechanisms and empirically evaluate the key determinants of successful AI-based CA interventions, spanning diverse mental health outcomes and populations. In this systematic review and meta-analysis, we synthesized evidence on the effectiveness and user evaluation of AI-based CAs in mental health care. Our findings suggest that these CAs can effectively alleviate psychological distress, with the most pronounced effects seen in studies employing generative AI, using multimodal or voice-based CAs, or delivering interventions via mobile applications and instant messaging platforms. CA-based interventions are also more effective among clinical and subclinical groups, and elderly adults. Furthermore, AI-based CAs were generally well-received by the users; key determinants shaping user experiences included the therapeutic relationship with the CA, the quality of content delivered, and the prevention of communication breakdowns.

Patient Physician Network Provides Financial Integration and Value-Based Care Support to Keep Independent Physicians Viable.

As investment pours in, the underlying technologies that fuel artificial intelligence are each seeing their own rocket blasts of innovation. Machine learning, deep learning, neural networks, generative AI—legions of researchers and developers are creating a remarkable profusion of generative AI use cases. In sum, the lifecycle for these AI companies is not so much digital transformation as digital revolution, and the next version of this list is likely to look completely different. With a strong reputation as a cybersecurity company with an advanced strategy, Palo Alto Networks’ AI-powered Prisma SASE (secure access service edge) solution is integrated with its Autonomous Digital Experience Management (ADEM) tool.

• Stanford Healthcare has also used machine learning models to coordinate in-patient care and reduce clinical deterioration events.
• Self-reported diabetes-related emotional distress was 3.6 points lower for the group using the conversational AI tool than those who did not.
• This can save the hours human operators have to give in for handling an ever-increasing number of calls and messages in healthcare systems.
• In the ensuing sections, we expound on these components and discuss the challenges that necessitate careful consideration and resolution.
• In the coming months, TELUS Health will launch new, intelligent automation functionality within the TELUS Collaborative Health Record (CHR) that leverages AI to empower healthcare professionals, patients and administrative staff.
• Its menu of enterprise AI solutions ranges from an AI chatbot to a platform that helps companies incorporate AI into enterprise applications.

Our findings provide valuable insights into the effectiveness of AI-based CAs across various mental health outcomes, populations, and CA types, guiding their safe, effective, and user-centered integration into mental health care. Digital oncology solutions can help to cut expenses, enhance patient care outcomes, and minimize physician burnout. AI has produced significant advances in healthcare delivery, notably conversational AI-based chatbots that inform cancer patients about clinical diagnoses and treatment options. However, the potential of AI chatbots to produce replies based on cancer knowledge has yet to be validated. Interest in deploying these technological advancements in patient-facing roles is considerable, but their medical accuracy, empathy, and readability remain unknown.

Insilico Medicine

According to the report, the administrative burden of healthcare is creating added confusion in an already complex industry. As Authenticx continues to invest in conversation data analysis, insights shared in the Customer Voices Report will continue to surface the intricacies of the healthcare business landscape and how voices can be a powerful data source to reshape healthcare. We are also working closely on Expanse integrations with Augmedix, as well as the Nuance DAX Copilot solution.

Executives from the company also provided additional details about its AI-driven nursing workflow collaboration with Epic during a media briefing on Tuesday. Their recommendations, which will be published in an upcoming issue of the Medical Journal of Australia, have informed a recently released national roadmap for using AI in health care. At MarketScale, we harness the power of our AI-driven platform alongside a vibrant community of B2B content creators. While Explainable AI methods have been developed to offer insights into how these systems generate their recommendations, these explanations frequently fail to capture the reasoning process entirely. Hatherley explained that this is similar to using a pharmaceutical medicine without a clear understanding of the mechanisms for which it works. “Everyone is excited about AI right now, but there are many open questions about how much we can trust it and to what extent we can use it,” Ana Catalina Hernandez Padilla, a clinical researcher at the Université de Limoges, France, told Medscape Medical News.

Career

Initial calls in the pilot will also be monitored by a human clinician to ensure patient safety. The expectation is that AI will manage over 85% of customer interactions in healthcare by 2025, reducing the need for human intervention and allowing healthcare professionals to focus more on patient care. This shift towards technology-dependent care teams emphasizes AI’s role as a partner in healthcare, enhancing our capabilities to serve and care. While technology won’t replace humans, it will become a more integral member of the care team. The future of care delivery will lie in a technology-dependent care team approach, where healthcare workers focus on their greatest comparative advantages over technology. In the quest for top-of-license care, clinician roles, decision making processes, and workflows will evolve by embedding this transformative technology.

The AI-enabled technology allows new mothers to ask these questions and receive intelligent, personalized responses that Penn Medicine has helped to inform as the clinical care team. “While undergoing many physical and emotional changes after birth, patients may also suffer complications such as infection, thrombosis and hypertensive disorders, as well as the new onset or exacerbation of mental health disorders and other chronic diseases,” she noted. The proposed metrics demonstrate both within-category and between-category associations, with the potential for negative or positive correlations among them. Within-category relations refer to the associations among metrics within the same category.

Interpretability ensures that the chatbot’s behavior can be traced back to specific rules, algorithms, or data sources46. However, they solely rely on surface-form similarity and language-specific perspectives, rendering them inadequate for healthcare chatbots. These metrics lack the capability to capture essential elements such as semantics19,20, context19,21, distant dependencies22,23, semantically critical ordering change21, and human perspectives, particularly in real-world scenarios.

When both intention-to-treat and completer analyses were reported, we extracted and analyzed the former. For studies with multi-arm designs that included multiple experimental or control groups, we combined the means and SDs from the different arms to create a single pair-wise comparison, as suggested by the Cochrane guidelines for integrating multiple groups from a single study69. If a study did not report sufficient data (mean, SD, SE, 95% CI) to calculate Hedges’g, we contacted corresponding authors for missing data; studies lacking necessary data were excluded from the meta-analysis. For sensitivity analysis, we employed a “leave-one-out” method70 to identify influential studies and assess the robustness of estimates. Furthermore, we conducted meta-analyses for specific psychological outcomes reported by at least three trials, including depressive symptom, generalized anxiety symptom, and positive affect and negative affect. Despite these potentially transformative applications, healthcare organizations must understand that generative AI will be only as good as the data it has been trained/fine-tuned upon.

The company is announcing its early adopters for ambient listening integration into its Expanse EHR; new functionality around its conversational AI functionality; and successful use cases from its early adopter of Expanse search and summarization with Google Health. Now, with the ability to learn from data and create something new, gen AI can not entirely replace doctors or do the work they do, but it sure can ease up the strained healthcare pipeline by augmenting certain aspects of the system. This can be anything from simplifying patient journeys and teleconsultation to handling clinical documentation and providing relevant information when the doctor is in surgery. These systems are like the cool kids on the block, giving us access to loads of text info and serving up conversations that actually make sense.

By collectively addressing these factors, the interpretation of metric scores can be standardized, thereby mitigating confusion when comparing the performance of various models. One primary requirement for a comprehensive evaluation component is the development of healthcare-specific benchmarks that align with identified metric categories similar to the introduced benchmarks in Table 2 but more concentrated on healthcare. These benchmarks should be well-defined, covering each metric category and its sub-groups to ensure thorough testing of the target metrics. Tailored benchmarks for specific healthcare users, domains, and task types should also be established to assess chatbot performance within these confounding variables.

Intrinsic evaluation metrics

Various prompting methods, such as zero-shot, few-shot, chain of thought generated with evidence, and persona-based approaches, have been proposed in the literature. Customizable digital humans — like James, an interactive demo developed by NVIDIA — can handle tasks such as scheduling appointments, filling out intake forms and answering questions about upcoming health services. The study will measure utilization, effectiveness, reliability, accuracy, empathy and patient perceptions of the AI tool.

More specifically, the company has worked on its GPU and storage connections and sophisticated network operating software. Tools like the Arista Networks 7800 AI Spine and the Arista Extensible Operating System (EOS) are leading the way when it comes to giving users the self-service capabilities to manage AI traffic and network performance. Founded in 2011, H2O.ai is another company built from the ground up with the mission of providing AI software to the enterprise. H2O focuses on “democratizing AI.” This means that while AI has traditionally been available only to a few, H2O works to make AI practical for companies without major in-house AI expertise.

Deployment Mode Analysis

Nuro is a robotics-focused company that uses AI, advanced algorithms, and other modern technology to power autonomous, driverless vehicles for both recreational and business use cases. The Nuro Driver technology is trained with advanced machine learning models and is frequently quality-tested and improved with rules-based checks and a backup parallel autonomy stack. The company partners with some major retailers and transport companies, ChatGPT App including Walmart, FedEx, Kroger, and Uber Eats. Part of this on-demand platform is a GPU offering that enables the rapid deployment of AI and machine learning tools. HPE focuses on providing AI geared for various verticals, from healthcare to financial services to manufacturing. Significantly, HPE and Nvidia recently announced a close partnership in which they will co-deliver several new enterprise-focused AI solutions.

Drug discovery is enormously expensive, and it’s typically met with low success rates, so AI’s assistance is greatly needed. Driving this development is the company’s mixed team of experts, including data scientists, bioengineers, and drug researchers. The AI company offers more than 150 stock AI avatars to allow users to create a virtual talking head using text prompts. To add realism, the avatars can be customized with facial gestures like raised eyebrows, head nods, and local languages and dialects. Considered one of the unicorns of the emerging generative AI scene, Glean provides AI-powered search that primarily focuses on workplace and enterprise knowledge bases.

Combining an app-based digital experience with mental health coaches certified by the National Board for Health & Wellness Coaching and trained in its proprietary, evidence-based mental health model, Wave has demonstrated significant reductions in acute depression, anxiety and stress. The company’s stepped care model, which delivers and monitors mental health treatment so that the most effective, yet least resource intensive treatment, is delivered first, also includes access to licensed therapists when clinically appropriate. Scale is an AI company that covers a lot of ground with its products and solutions, giving users the tools to build, scale, and customize AI models—including generative AI models—for various use cases. The Scale Data Engine simplifies the process of collecting, preparing, and testing data before AI model development and deployment, while the Scale Generative AI Platform and Scale custom LLMs give users the ability to fine-tune generative AI to their specifications. Scale is also a leading provider of AI solutions for federal, defense, and public sector use cases in the government.

Clinicians should be given training on how to critically assess AI applications to understand their readiness for routine care. Many claims made by the developers of medical AI may lack appropriate scientific rigour and evaluations of AI tools may suffer from a high risk of bias. With new language-based generative AI technologies like ChatGPT, the clinical world is abuzz with talk of chatbots for answering patient questions, helping doctors take better notes, and even explaining a diagnosis to a concerned grandchild.

In essence, BigPanda uses machine learning and automation to extend the capabilities of human staff, particularly to prevent service outages. At the center of today’s enterprise cyber protection is the security operations center (SOC). Fortinet’s automated SOC uses AI to ferret out malicious activity that is designed to sneak around a legacy enterprise perimeter. The strategy is to closely interoperate with security tools throughout the system, from cloud to endpoints. In June 2024, Fortinet announced that it would be acquiring Lacework, a leading provider of AI-powered cloud, code, and edge security solutions.

• Dr. Shreya Shah, a practicing academic internist, board certified practitioner in clinical informatics and expert in AI healthcare integration at the health system presented how the model works at the HIMSS AI forum.
• Founded in 2017, Black in AI is a technology research and advocacy group dedicated to increasing the presence of black tech professionals in artificial intelligence.
• Despite its potential, AI in medicine presents several risks that require careful ethical considerations.
• Recent advancements in artificial intelligence (AI), such as natural language processing (NLP) and generative AI, have opened up a new frontier–AI-based CAs.
• Despite word count regulation efforts, only the third chatbot response showed higher word counts than physician replies.

More recently, the vendor has come out with Ironclad Contract AI, an AI assistant that supports users with chat-driven solutions for additional contract tasks and queries. Conversational AI is adaptive technology that utilizes machine learning, artificial conversational ai in healthcare intelligence, and natural language processing (NLP) to understand human language and user intent. NLP allows a computer system to interpret voice or written language, deciphering its meaning without relying on correct grammar and syntax.

The company was founded by many former leaders from DeepMind, Google, OpenAI, Microsoft, and Meta, though several of these leaders have since left to work in the new Microsoft AI division of Microsoft. It’s truly up in the air how this change will impact the company and Pi, though they expect to release an API in the near future. It says this tool increases access for those who may have language barriers or difficulty accessing the system’s patient portal, MyWellSpan. Plans are in development to launch additional languages spoken in other communities the health system serves, including Haitian Creole and Nepali. The racially inclusive voice user interfaces in the UpToDate engagement programs are among the first in the healthcare ecosystem. As Feldman notes, the only voice choice in most commercial user interfaces is gender, though a couple of platforms are exploring a racial options.