How GenAI Adoption Might Change The Face Of Indian Healthcare

If the data is not prepared well or carries any kind of biases, the outcomes of the models will also reflect those problems, hitting the reputation of the business. With a gen AI-driven approach, teams could fine-tune models like GPT-4 vision and use them to study and generate reports from medical data, automating and accelerating the entire process for good. Yes, the idea is still fresh, but early experiments show it is a promising application of gen AI in healthcare. In fact, a study by JAMA Network found that AI-generated reports for chest radiographs had the same level of quality and accuracy as those produced by human radiologists. Among the providers using conversational AI agents are Mercy Health, Baptist Health, and Intermountain Healthcare. They all have launched bots for automating tasks like patient registration, routing, scheduling, FAQs, IT helpdesk ticketing, and prescription refills.

Further, many have even started deploying gen AI copilots that listen to the conversation between the patient and physician and generate summarized clinical notes, saving doctors the trouble of documenting and filing the information manually in an EHR. Nabla, one of the providers of such copilots, even uses these notes to generate a set of patient instructions, on behalf of the physician. This capability can be further developed into a gen AI system that sits alongside the doctor and creates personalized treatment and therapy plans based on the current conditions as well as previously recorded parameters, including genetic makeup, health history, and lifestyle.

Consumers say the communication skills, care and touch of a health professional are especially important when feeling vulnerable. If health services are already discriminatory, AI systems can learn these patterns from data and repeat or worsen the discrimination. The primary study outcomes included pilot evaluations for readability, empathy, and quality on Likert scales ranging between 1.0 (extremely poor) and 5.0 (very good). Physicians ChatGPT App from radiation oncology, medical oncology, and palliative and supportive care graded quality, empathy, and readability. The secondary outcome was readability, measured using Flesch-Kincaid Grade Level (FKGL) scores, Gunning-Fog Index, and Automated Readability Index. The researchers compared AI chatbot replies with responses from six confirmed doctors to 200 cancer-related queries posed by patients in a public forum.

Essential metrics for evaluating healthcare chatbots

NLU technology enables a virtual agent to use sentiment analysis, which helps reps monitor the emotions of callers. From its initial start in conversational AI, Amelia has since expanded into AIOps and Amelia Answers, an AI-powered enterprise search solution. Oracle’s cloud platform has leapt forward over the past few years—it’s now one of the top cloud vendors—and its cloud strength will be a major conduit for AI services to come. To bulk up its AI credentials, Oracle has partnered with Nvidia to boost enterprise AI adoption. The company stresses its machine learning and automation offerings and also sells a menu of prebuilt models to enable faster AI deployment. Feldman indicates that the healthcare industry can’t create these tailored, racially inclusive VUIs without implementing the same safeguards utilized to protect patient privacy in other healthcare systems.

• “You have to have a human at the end somewhere,” said Kathleen Mazza, clinical informatics consultant at Northwell Health, during a panel session at the HIMSS24 Virtual Care Forum.
• The award, which included a cash prize, recognizes educational institutions that inspire and support students in choosing engineering and technology as their preferred career paths….
• This technology works to support observability, a growing trend in infrastructure security.
• The business model involves using machine learning models to forecast financial megatrends.
• “First, a frequently asked question bank was used to generate accurate mapping of questions to the appropriate responses,” Leitner explained.
• Further, many have even started deploying gen AI copilots that listen to the conversation between the patient and physician and generate summarized clinical notes, saving doctors the trouble of documenting and filing the information manually in an EHR.

The study selection process, data extraction, and risk of bias assessment were carried out by H.L. Additional time savings Mile Bluff realized included time spent reconciling problem lists, locating DNR orders, and streamlining infection control chart reviews. The solution is now used across 19 departments – clinical and nonclinical settings – including obstetrics, infusion and cancer care, infection control, revenue cycle, and diabetes education. We see great promise in the use of ambient listening across care settings, including future incorporation into our home care and nursing solutions. As ambient listening is further adopted across more care settings, we see even greater promise in reducing the documentation burden for more care providers. This year, not surprisingly, its biggest focus is on artificial intelligence – the hottest topic in healthcare information technology.

The company offers accessible AI algorithms for optimized clinical trials, particularly for oncology, as well as AI-powered companion diagnostics, pre-screening predictions, spatial analyses, and translational research. The company’s algorithms and products specifically support biomarker quantification for various cancers, disease severity assessments, quality control, tumor cellularity quantification, and molecular prediction. To help call center reps boost performance with customer calls, boost.ai provides agents with a large repository of support data.

Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology

Even after rapid digitization, most diagnostic agencies today rely on human experts to study medical images and write reports for patients. The work takes a lot of time and effort and is even prone to errors stemming from inherent biases or just basic human tiredness. It is feasible to train LLMs using real-world dialogues developed by passively collecting and transcribing in-person clinical visits, however, two substantial challenges limit their effectiveness in training LLMs for medical conversations. First, existing real-world data often fails to capture the vast range of medical conditions and scenarios, hindering the scalability and comprehensiveness.

The generative AI landscape in particular changes daily, with a slew of headlines announcing new investments, fresh solutions, and surprising innovations. Focusing on the K-12 market, Carnegie Learning’s MATHia with LiveLab is well recognized as an advanced AI learning app. The app uses an AI-powered cognitive learning system to support math education, offering students one-on-one interactions that allow them to work at a pace that best suits their conversational ai in healthcare skill level. Already a large and well-established medical device maker, in 2021, Stryker acquired the AI company Gauss Surgical and is aggressively moving to deploy AI more broadly across its product offerings. Among its notable products is the AI-based Stryker Mako robot, which can assist with numerous medical procedures. Deepcell is a biotech startup—spun out of Stanford University in 2017—that leverages AI to examine and classify cells.

If we draw together international evidence, including our own and that of others, it seems most consumers accept the potential value of AI in health care. Host Kevin Stevenson talks with the heroes behind the heroes that are enabling hospitals, urgent care centers and telemedicine operators to spend their time tending to patients, while they handle the logistics. She said while younger clinicians might be more open to testing the waters with AI tools, older practitioners still prefer to trust their own senses while looking at a patient as a whole and observing the evolution of their disease. “They are not just ticking boxes. They interpret all these variables together to make a medical decision,” she said.

Within the openCHA framework, this capability allows for the decomposition of user queries into manageable subproblems, facilitating the execution of tasks required to gather pertinent information. Once all relevant data is collected, the second LLM takes charge, utilizing the amassed information to furnish users with reliable answers. They’re powered by those trusty LLMs, making sure they understand you and can give you the personalized support you need, whether it’s answering your burning health questions or just lending an empathetic ear. A. In the realm of healthcare, the abundance of misinformation can leave individuals feeling lost and uncertain.

SafeRead produces ‘actionable data from patients’

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. The second crucial requirement involves creating comprehensive human guidelines for evaluating healthcare chatbots with the aid of human evaluators. Healthcare professionals can assess the chatbot’s performance from the perspective of the final users, while intended users, such as patients, can provide feedback based on the relevance and helpfulness of answers to their specific questions and goals.

Users can also take assessments that ELSA’s AI uses to customize courses and learning timelines that fit that particular user. A leading player in the accounts receivable automation software sector, HighRadius uses machine learning to help with labor-intensive tasks like matching payments with invoicing and assigning credit limits. Bank of America, in a breathless note to the investment community, opined that “AI is the new electricity.” So what exactly does this look like in an industry that is riddled with regulations, complexities, and longtime, established vendors that may be hesitant to try something new? ClosedLoop’s data science platform leverages AI to manage and monitor the healthcare landscape, working to improve clinical documentation to lower out-of-network use and predict admission and readmission patterns. Impressively, the company won the CMS Artificial Intelligence Health Outcomes Challenge in 2021. Owkin uses AI to drive predictive analytics for the development of better drug solutions for a variety of diseases.

You can foun additiona information about ai customer service and artificial intelligence and NLP. A leader in data analytics and business intelligence, SAS’s AI menu extends from machine learning to computer vision to NLP to forecasting. Notable tools include data mining and predictive analytics with embedded AI, which boosts analytics flexibility and scope and allows an analytics program to “learn” and become more responsive over time. The ultimate legacy software player, known for its strength in ERP, SAP has clearly moved into the AI era. Its menu of enterprise AI solutions ranges from an AI chatbot to a platform that helps companies incorporate AI into enterprise applications.

This feature was key, the researchers stressed, because they prioritized placing clinical decision-making with providers, not with AI. There are computer vision tools that can detect suspicious skin lesions as well as a specialist dermatologist can. Artificial intelligence (AI) seems to be everywhere these days, and healthcare is no exception. A division of BlackBerry, Cylance AI touts its “seventh generation cybersecurity AI.” Due to its extended lifecycle in use by clients, the AI platform has been trained on billions of cyberthreat datasets.

In the ensuing sections, we expound on these components and discuss the challenges that necessitate careful consideration and resolution. The FLoating point OPerations (FLOP) metric quantifies the number of floating point operations required to execute a single instance of healthcare conversational models. This metric provides valuable insights into the computational efficiency and latency of healthcare chatbots, aiding in their optimization for faster and more efficient response times. The Interpretability metric assesses the chatbot’s responses in terms of user-centered aspects, measuring the transparency, clarity, and comprehensibility of its decision-making process45. This evaluation allows users and healthcare professionals to understand the reasoning behind the chatbot’s recommendations or actions. Hence, by interpretability metric, we can also evaluate the reasoning ability of chatbots which involves assessing how well a model’s decision-making process can be understood and explained.

In short, organizations can use AI tools to help automate aspects of their customer communication while preserving and even augmenting the personal touch. For example, healthcare providers can deploy generative AI to create tailored messages and develop new content that meets individual patient needs. Lastly, AI can also aid in refining data segmentation, allowing operators and healthcare providers to construct a more precise understanding of their users. Though implementation challenges remain, GenAI-led developments are all set to take the Indian healthcare industry by storm. According to an EY report, pharmaceutical companies should invest in cybersecurity measures like encryption, secure data hosting, and privilege access management to mitigate the risk of IP theft, given the GenAI models are trained on the organisation’s clinical data.

AI in Medicine Sparks Excitement and Concerns Among Experts

Despite word count regulation efforts, only the third chatbot response showed higher word counts than physician replies. The first (mean, 12) and second chatbot replies (mean, 11) had considerably higher FKGL ratings than physician replies (mean, 10), whereas the third chatbot replies (mean, 10) were comparable to physician responses. However, physician replies had a 19% lower readability rating (mean, 3.1) than chatbot 3, the best-performing chatbot (mean, 3.8). UTDesign took home the Tech Titans of the Future – Community College/University award at the Tech Titans Gala 2024.

From cash flow crises to acquiring new clients in a nascent market and surviving economic downturns, MicroGrid dealt with all. The company has made 3x year-on-year growth in revenue (USD 1.65 M), which signifies robust market acceptance and the effectiveness of the business model. “Steadily moving closer to MicroGrid’s vision of redefining conversations across the globe,” said Vedula. AI requires local customisation to support local practices, and to reflect diverse populations or health service differences. We don’t want to just export our clinical datasets and import back the models built with them without adapting to our contexts and workflows.

The tools can also leverage unified healthcare data and care management analytical templates to enhance patient care by identifying high-risk individuals, optimizing treatment plans and improving care coordination, the company said. Using native analytical tools in Azure and Fabric, healthcare organizations can analyze the data and combine it with other patient data, such as EHR data and patient engagement insights to create comprehensive ChatGPT data. The WHO’s new tool, the Smart AI Resource Assistant for Health, or Sarah, has encountered issues since its launch. The AI-powered chatbot offers health-related advice in eight languages, covering subjects such as healthy eating, mental health, cancer, heart disease and diabetes. Developed by the New Zealand company Soul Machines, Sarah also incorporates facial recognition technology to provide more empathetic responses.

Narrative synthesis of user engagement and experience

“In addition to blood pressure screening, making it easy for patients to ask questions about symptoms has also allowed us to detect this potentially serious condition.” Through ongoing training of Penny and the underlying technology, Penn Medicine has seen more than 70% of patient questions correctly answered by conversational AI. “While we of course recognize that automated processes sometimes have kinks, we’ve made sure to plan for these,” she added. “Our team has built ways to ensure that responses are accurately reflective of what patients expect to receive from their doctor.”

EWeek stays on the cutting edge of technology news and IT trends through interviews and expert analysis. Gain insight from top innovators and thought leaders in the fields of IT, business, enterprise software, startups, and more. A prime example of an AI vendor for the retail sector, Bloomreach’s solutions include Discovery, an AI-driven search and merchandising solution; and Engagement, a consumer data platform.

• The idea of AI often elicits either excitement or fear, and there is cause for both, Feldman says.
• Consumers are concerned AI will take the “human” elements out of health care, consistently saying AI tools should support rather than replace doctors.
• Accuracy metrics are scored based on domain and task types, trustworthiness metrics are evaluated according to the user type, empathy metrics consider patients needs in evaluation (among the user type), and performance metrics are evaluated based on the three confounding variables.
• The net result is that AI helps human security admins with observability across their infrastructure, which is crucial for enterprise security.
• Patients often must trust the virtual assistant with personal, sensitive, and sometimes embarrassing information to get the needed services or information.
• This evaluation allows users and healthcare professionals to understand the reasoning behind the chatbot’s recommendations or actions.

It is important to note that performance metrics may remain invariant concerning the three confounding variables (user type, domain type, and task type). In the following sections, we outline the performance metrics for healthcare conversational models. General-purpose human evaluation metrics have been introduced to assess the performance of LLMs across various domains5. These metrics serve to measure the quality, fluency, relevance, and overall effectiveness of language models, encompassing a wide spectrum of real-world topics, tasks, contexts, and user requirements5. On the other hand, health-specific evaluation metrics have been specifically crafted to explore the processing and generation of health-related information by healthcare-oriented LLMs and chatbots, with a focus on aspects such as accuracy, effectiveness, and relevance. Second, it is evident that the existing evaluation metrics overlook a wide range of crucial user-centered aspects that indicate the extent to which a chatbot establishes a connection and conveys support and emotion to the patient.

It is important to carefully examine how AI tools are embedded into workflows to support clinical decisions. The benefits and risks of a tool will depend on precisely how the human clinician and the tool work together. In our recent article in the Medical Journal of Australia, we argue using AI effectively in healthcare will require retraining of the workforce, retooling health services, and transforming workflows. The progress of artificial intelligence won’t be linear because the nature of AI technology is inherently exponential. Today’s hyper-sophisticated algorithms, devouring more and more data, learn faster as they learn.

The net result is that AI helps human security admins with observability across their infrastructure, which is crucial for enterprise security. Winner of Time Magazine’s Best Inventions award in 2021, Amira Learning uses an AI-powered gamified learning environment to improve reading skills. Children read aloud as Amira provides real-time support; the solution has multiple tutoring techniques to coach young readers, including offering encouragement.