A Comparative Analysis of AI-Based Chatbots for Disease Diagnosis Based on Symptoms

Abstract

Investment in artificial intelligence (AI) chatbots within the United States has experienced substantial growth in recent years, driven by the increasing demand for intelligent virtual assistants across various sectors. By 2025, the United States is anticipated to be a significant contributor to the global AI chatbot market, which is projected to expand from $8.6 billion in 2024 to $11.14 billion in 2025 [1]. This growth represents a compound annual growth rate (CAGR) of 29.5% [1]. The expansion is primarily attributed to the rising demand for automated customer support, personalized digital experiences, and the widespread implementation of AI technologies. Major technology companies and venture capital firms are making noteworthy investments in the development of AI chatbots. For instance, Gloo, a technology firm focused on faith-based solutions, raised $110 million to advance AI tools [2], including chatbots. Concurrently, Yutori, a startup founded by former executives from Meta AI, secured $15 million to develop sophisticated AI personal assistants [3]. On a broader scale, prominent U.S. technology firms, including Microsoft, Amazon, and Google, are investing hundreds of billions of dollars into AI infrastructure and applications, with a focus on chatbot technologies. Microsoft, for instance, has outlined a plan to allocate $80 billion to AI initiatives in 2025 [4], while Amazon has invested $8 billion in Anthropic, a leading AI startup specializing in generative AI and chatbot solutions [5]. These investments underscore the strategic significance of AI chatbots in enhancing customer engagement, optimizing operations, and fostering innovation across various industries, such as healthcare, finance, retail, and education. As AI capabilities continue to advance, the United States is poised to maintain its leadership in chatbot innovation and commercialization. Artificial Intelligence (AI)-driven chatbots have emerged as transformative tools within the healthcare sector, playing an increasingly vital role in facilitating symptom-based disease diagnosis. These intelligent systems harness a suite of advanced AI technologies—including machine learning, natural language processing (NLP), and knowledge representation frameworks—to interpret patient-reported symptoms, assess potential health conditions, and provide users with preliminary diagnostic insights [6], [7].This white paper offers a comparative evaluation of the capabilities, strengths, limitations, and practical applications of leading AI-powered chatbots specifically designed for disease identification. It explores the algorithms and datasets that drive their diagnostic reasoning, the accuracy and reliability of their outputs, and their ability to adapt across varying clinical scenarios and patient populations [8]. The paper highlights the critical role of AI chatbots in expanding access to healthcare, particularly for underserved or remote communities where traditional medical resources may be limited [9]. By offering on-demand symptom assessment, triage guidance, and referral recommendations, these systems empower individuals to seek timely medical attention, potentially reducing the burden on emergency departments and primary care providers. In addition to assessing current implementations, this paper addresses key challenges—including data privacy, diagnostic accuracy, regulatory compliance, and patient trust—that impact the broader adoption and integration of AI chatbots in clinical practice [10]. It explores how these technologies could evolve to support predictive analytics, personalized medicine, and integrated care pathways, ultimately contributing to more efficient, responsive, and patient-centered healthcare systems [11].