Improved patient experience is shown to dramatically improve patient health outcomes and health insurance systems [12,13]. AI-driven systems can curate a personalized experience, deliver efficient triage and intake, and provide educational interventions that encourage early engagement, improve accessibility, and close gaps in care [14,15]. By using an agentic AI approach, it further removes any stigma that men may feel by trying to identify their specific needs and catering to their approach [16,17]. Examples include customizing an intake form as a conversation, providing real-time educational information, suggesting questions that the patient may ask during their visit based on information they have provided, providing validation and encouraging conversation, and others.

AI tools have demonstrated the capacity to positively impact population health [18,19]. By improving efficiency, identifying at-risk populations, enhancing research, and streamlining workflows, AI can contribute to better safety, quality, and consistency of care in health care delivery [20]. Rather than relying on reports, AI can assist in proactively working with the physician in order to address population health, including prompts, suggestions, preparation of orders, and more [21,22]. AI can also evaluate individual demographic data, reviewing it against clinical studies to identify whether population health data will apply to a specific individual and their medical context [23,24].

$760 to $900 billion USD is considered to be due to waste in the US health care system [25]. Predictive and preventative care systems can minimize avoidable acute care, address costly chronic diseases early, and reduce redundant testing [26,27]. AI-driven systems can assist in identifying disease and optimized care navigation to ensure accurate and necessary follow-up, preventing excessive services [28,29]. Furthermore, AI tools can assist in administrative tasks, improving provider efficiency and ensuring consistently accurate billing and coding [30,31]. Examples include ensuring laboratory results are available at the time of office visits, following through on referrals, querying databases to ensure clinical data is synthesized for ease of physician use, and others, contributing to a potential US $200-$360 billion in savings on health care spending [32].

AI tools can improve health equity by increasing access, breaking down language barriers, and supporting targeted interventions [33,34]. Through large-scale data analysis, AI tools can detect inequities in resource allocation and direct health care strategies towards marginalized populations [34-36]. Virtual, mobile, and telehealth platforms offer a scalable mechanism to provide access to patients typically excluded from traditional care delivery systems [37,38].

AI-driven systems reduce administrative load, streamline navigation, and facilitate more timely and personalized care delivery [15,38]. While patients benefit from timely and personalized care delivery that improves engagement and outcomes, providers get to experience a much more meaningful interaction with their patients by removing clerical and administrative tasks so that their focus is on the clinical interaction and rapport building, augmented by AI prompts and suggestions from clinical studies [30]. In this way, AI tools can improve provider efficiency, improve diagnostic accuracy, and reduce the burden of administrative tasks and documentation to decrease rates of burnout [15,39].

Engagement agents interact with patients through personalized communication with the goal of improving patient adherence to care plans, enhancing patient education, and providing reminders [51,52]. Current chatbots can tailor their communication to the preference and style of patients to keep conversations relevant and engaging for the patient [52]. In addition, these agents could cross language barriers by providing real-time translation between languages and adjusting the information presented to educational and reading levels [53]. They can facilitate patient participation in their health, encouraging patients to take preventative measures and easing the burden on providers [51]. Engagement agents utilizing these tools demonstrate the ability to have feasible and positive effects on patient outcomes [14,54].

One example of such a feature is Woebot, a LLM-based chatbot for patient engagement that analyzes emotional states in the conversation and makes adjustments to deliver context-aware responses in real time [51]. Technologies such as these demonstrate the ability to maintain quality and empathetic care delivery while easing the burden on providers [14,55]. A practical example in a urologic setting would be a patient portal, in which a patient can exchange secure messages with an engagement agent. As seen in a Stamford University Study, messages can be categorized based on the type of message (results, medications, schedule, and general) and interpreted in the context of the patient’s medical record [56]. New information can be sent to the electronic health record through an API call. Agent-suggested changes to the medical record (medication history, medical history, compliance, symptoms, or diagnosis) can be updated with a provider’s approval. At the same time, a relevant reply can be drafted or sent automatically to guide a patient to the next step in their care plan.

Medical history agents describe AI tools that modernize and streamline the history-taking process for patients and provide thorough and relevant information as necessary to providers [57]. These chatbots present patients with targeted, personalized questions, providing an accessible and attractive option for history taking [57,58]. They specialize in organizing relevant information for providers to deliver a summary that can quickly prepare a provider for a clinical encounter [54]. These agents are essential for the functions of an agentic health care practice, providing relevant information as needed for both caretakers and other agents.

In a systematic review of studies on AI chatbots, Hindelang et al [57] review the uses of different chatbots in clinical settings. A study on a medical history bot used to support anamnesis could reduce time spent on history in patient encounters by over 50% [59]. Another example of a medical history agent is AnCha, an AI interviewer that supports the collection of history and current complaints for a patient. Previsit documentation of relevant medical history could prime patients for clinical encounters and provide information for upstream use where higher priority items are given more emphasis to reduce visit time. Future versions of AnCha or similar bots could solve language barriers or present questions with images that patients can interact with, further increasing accessibility and efficacy of history taking [54,57,60]. The unique option for providers to let patients know an AI agent is being used may further remove stigma or hesitation from patients due to less fear of being judged or “labeled.” In urologic practice, previsit agents could systematically capture smoking history, occupational exposures, and anticoagulant use for hematuria evaluations; gonadotoxin and surgical history for male infertility consultations; and International Prostate Symptom Score and prior procedural history for Benign prostatic hyperplasia BPH assessments, reducing intake time while improving completeness.

Triage agents are designed to automate or support staff in determining the urgency and priority of care to appropriately designate resources based on a patient’s history and current status. They can work with medical history agents to review patient backgrounds, symptoms, and vital signs to assign triage categories or recommend care plans. They are essential as a prerequisite for further care delivery, decision support, or patient-facing digital assistants that collect patient histories [46,54,61-64]. They have demonstrated efficacy and efficiency in rapidly triaging patients, reducing wastefulness of resources, and minimizing undertriaging and overtriaging [61].

In 1 study, a novel triage prediction AI model was trained to analyze prior patient data retrospectively to determine if it could more precisely and accurately determine potential health outcomes. Emergency room physicians were asked to predict the criticality of 30 visits extracted from a dataset of 171,275. The AI model outperformed physicians in both categories in determining criticality of health outcomes, with a sensitivity of 95% compared to physicians’ 41%, and an accuracy of 90% compared to physicians’ 67%. While the study was based on retrospective data in China and lacked a validation cohort, it demonstrates the potential of predictive analysis in AI tools [65]. In a similar separate study, researchers trained an AI model to review patient medical information to provide an accurate Emergency Severity Index (ESI) value. Nurses were asked to assign an ESI value from 800 samples from a dataset of 147,052. The model estimated with an accuracy of 75.9%, compared to nurses with 59.8% and study clinicians with 75.3%, with fewer errors and more accurate high-risk recognition [66]. Both studies were prospective and lacked a validation cohort, which questions their efficacy in other clinical settings. However, both studies demonstrate the potential for triage agents to efficiently and accurately assess patient priority, reducing the burden on clinicians, increasing accuracy of triage, and reducing costs through effective resource allocation. While this approach does raise questions about scope of practice and triage, which is regulated in many states, it allows for a first pass of information, though it will require some medical-legal input to determine who ultimately holds the responsibility of inappropriate triage. In urology, triage agents could differentiate obstructing infected ureteral stones from uncomplicated colic based on fever, urinalysis, and symptom pattern, and could screen postoperative patients following endoscopic or robotic procedures for red-flag symptoms warranting urgent provider notification.

Decision support agents can assist in the diagnosis and treatment of complex or rare clinical conditions based on clinical guidelines and contribute to multidisciplinary patient case management. They can help establish personalized treatment plans and act as a resource to physicians in increasing efficiency and accuracy of care delivery [67]. By analyzing large volumes of health data, these agents can evaluate risk predictions, provide diagnostic support, and adapt to new information in more complex clinical situations [67]. This has allowed for the development of AI tools that provide advice for clinical decision-making, indicative of future health care agents that can assist physicians through clinical reasoning, acting as a well-versed peer to support personalized patient care [68].

AI-based tools have been used in numerous clinical situations for predictive or early diagnostic support [21,69-71]. In urologic settings, AI tools have applications for prostate brachytherapy, biopsy, cancer diagnosis, and cancer staging [72-74]. They have demonstrated the ability to analyze and grade radiomics features of renal cell carcinoma patients with comparable performance to renal biopsy, reducing the need for invasive alternatives [47]. Furthermore, they have demonstrated efficacy in estimating the response of a bladder cancer lesion to chemotherapy, matching or outperforming estimations of radiologists [75]. These agents may also be able to look at chemotherapeutic options for patients by analyzing pathology and cytology in the setting of clinical studies and case reports, as well as cancer recurrence predictions.

Documentation agents support health care providers by capturing audio in clinical settings, transcribing text, and generating drafts of clinical notes for physician review [76-78]. These agents can dramatically reduce the amount of time providers spend on documentation, allowing providers to be more engaged in patient conversation [76]. These agents could further surpass the capabilities of traditional LLMs by placing orders with proper billing and coding in place for physicians to review [22]. In a space where documentation burdens directly contribute to provider burnout, documentation agents can fill a critical need for providers to improve care delivery and health outcomes [79].

Amongst the more than 90 ambient scribing platforms that exist at the time of this article’s publishing, 1 study reviewed DAX-copilot, an AI-powered clinical documentation agent owned by Microsoft [76,78]. Researchers were interested in the potential benefits of the tool, the types of encounters fitting for AI technology, and physician impressions on AI-facilitated clinical documentation. The study found that providers saw a significant reduction in time spent on clinical documentation and cognitive tasks, allowing for more time to be spent on engaged care delivery. While the providers had some hesitance towards using the AI agent in some situations, and documentation notation occasionally fell short of standards, physicians recognized the potential for the tool to improve their quality of life, reduce their daily cognitive burden, and improve quality of engagement with patients [76]. Microsurgical operative reports for procedures such as varicocelectomy, vasectomy reversal, and spermatic cord denervation require detailed anatomic narrative and current procedural terminology (CPT) justification. Documentation agents could generate structured drafts from intraoperative dictation, reducing post-case charting burden and improving coding accuracy.

Amongst the more than 90 ambient scribing platforms that exist at the time of this article’s publishing, 1 study reviewed DAX-copilot, an AI-powered clinical documentation agent owned by Microsoft [76,78]. Researchers were interested in the potential benefits of the tool, the types of encounters fitting for AI technology, and physician impressions on AI-facilitated clinical documentation. The study found that providers saw a significant reduction in time spent on clinical documentation and cognitive tasks, allowing for more time to be spent on engaged care delivery. While the providers had some hesitance towards using the AI agent in some situations, and documentation notation occasionally fell short of standards, physicians recognized the potential for the tool to improve their quality of life, reduce their daily cognitive burden, and improve quality of engagement with patients [76]. Microsurgical operative reports for procedures such as varicocelectomy, vasectomy reversal, and spermatic cord denervation require detailed anatomic narrative and CPT justification. Documentation agents could generate structured drafts from intraoperative dictation, reducing post-case charting burden and improving coding accuracy.

Care navigation describes guiding patient progress throughout their larger health care journey, ensuring that transitions between different aspects of care are seamless and continuous. Care navigation agents support patients and assist caretakers in adequate preparation for care delivery, navigating follow-ups, and ensuring positive outcomes [67,80]. They can schedule appointments or referrals and share information with relevant providers to ensure that patients have proper follow-up with positive outcomes [14,80]. As conversational agents or chatbots, they can provide greater accessibility to patients with limited access to in-person care while maintaining a personalized experience, allowing for caretakers to support larger populations of patients more effectively [14]. They can consider the context of a patient’s environment or social determinants of health to identify social needs [14,81].

One study used a care navigation agent aware of a patient’s clinical, socioeconomic, and behavioral data to predict the likelihood of patient readmission and provide care navigators with tips to prevent rehospitalization [82]. Out of over 6000 encounters, the AI identified 29.3% as medium to high risk for rehospitalization within 30 days, and provided recommendations for care navigators. Of the high-risk adults provided recommendations, the navigation team saw a 21% decrease in incidence of 30-day rehospitalization, or 69 fewer rehospitalizations for every 1000 encounters [82]. With a statistically significant sample size, the study demonstrates the capabilities of care navigation agents to reduce health care costs by minimizing unnecessary rehospitalizations while improving transitional care outcomes. Care navigation agents may also be able to provide resources to address social determinants of health, including financial support for medications, utilities, as well as links to various social networking options and support groups that may be a good fit for an individual [81,83]. Nonmuscle invasive bladder cancer surveillance, which requires cystoscopy and cytology at defined intervals over years, is an ideal navigation use case. Agents could manage scheduling, coordinate pathology review, and alert providers to findings meeting criteria for disease progression or treatment escalation.

Health and wellness coaching agents provide personalized, holistic, interactive care through chatbots or virtual assistants to support lifestyle behavior change for patients coping with chronic issues outside of the hospital. These AI agents read patient data to deliver a tailored experience through personalized goal setting, real-time feedback, motivational support, medical education on lifestyle and behavioral changes, and stress management. By providing consistent availability for patients automatically, or by supporting human coaches with suggestions and quality control, these agents provide scalable, accessible, and on-demand services through virtual support platforms [84-86].

A multitude of health and wellness coaching AI support agents exist in the current health care market, such as Tess (X2AI Inc), Lark (Lark Health), and Woebot (Woebot Health), conversational AI that support patients in lifestyle and behavior change [67,87-89]. These agents have demonstrated the ability to drive engagement with care plans and healthier lifestyle behaviors to reach and maintain health goals reviewed by providers. One study specifically examined how a virtual chatbot, Paola (University of South Australia), could support patients in increasing their exercise, maintenance to a Mediterranean diet, and losing weight [87]. Compared to the baseline, these patients completed 109 more minutes of exercise after 12 wk, lost 1.3 more kg of weight, and lost 2.1 cm from their waistline [87]. Increased health and wellness coaching services are already being used at scale to support patient care outside of the clinic, and these agents demonstrate the potential to help patients succeed in reaching goals in an accessible, low-cost, and scalable process. Obesity, metabolic syndrome, and physical inactivity are modifiable risk factors for erectile dysfunction and hypogonadism (Hehemann and Kashanian 2016). Coaching agents could deliver structured behavioral interventions targeting these factors between visits, reinforcing lifestyle changes that complement pharmacologic or hormonal treatment.

One of the challenges in remote patient monitoring is the amount of incoming data and the ability to appropriately spend time reviewing and synthesizing the various data inputs in a meaningful way for clinical decision-making. Ongoing patient monitoring post care delivery is essential for quality control, quality improvement, and ensuring safety of interventions. To meet the need for this quality assurance, patient monitoring agents can leverage ongoing changes in patient data from electronic health records, wearable devices, and digital sources to recognize deviations in expected outcomes, poor adherence to treatment recommendations, safety issues, and support quality improvement [90,91]. Patient monitoring agents can be used to predict when changes in patient health status increase risk, alerting caretakers or other agents when interventions may be necessary [92]. As AI algorithms become more widely used in health care settings, traditional methods of quality and outcome monitoring may not be able to maintain scalability or allow for wider testing of intervention strategies [48,90]. Outcome monitoring agents could be effective support resources that ensure scalable health care delivery systems are continually delivering quality care, and that interventions are effective and safe while upholding ethical and clinical standards [93].

One study proposed the use of AI as a tool for both reliably communicating and effectively predicting changes in vital signs of a critical patient [91]. The study found that an AI-powered tool was able to effectively decrease the probability of communication failures between monitoring devices and alarm systems. Furthermore, they demonstrated that the tool was capable of predicting both normal and critical fluctuations in data from patient monitoring devices in as little as 25 ms. AI tools such as these demonstrate the capability to improve upon current remote and local patient monitoring processes, as well as enhancing other aspects of the agentic care delivery system by rapidly predicting changes in patient status [91]. This information can be shared with care navigation agents and health and wellness coaching agents to ensure proper and effective follow-up. Following radical prostatectomy, monitoring agents could track patient-reported catheter output and hematuria. During androgen deprivation therapy, agents could survey for metabolic complications including glycemic changes and weight gain, alerting providers when intervention thresholds are met.

Administrative agents can automate or augment the nonclinical, operational, or organizational tasks within health care systems to increase provider efficiency [30,94,95]. They can support nurses in streamlining processes such as patient admissions, transfers, and discharges, saving 37%‐46% of time spent on those tasks [94]. They further can improve the efficiency of billing processes by expediting the reimbursement process, ensuring proper coding, detecting fraud, and even revenue optimization [30,96,97]. Administrative AIs have shown to reduce coding errors and manual workload, improving efficiency and mitigating financial losses [97,98]. While LLMs alone have the potential to expedite 15% of labor tasks, tool-enhanced LLMs like administrative agents increase that percentage to over 46% [99].

A study discussed how the time-intensive and high-pressure environment of the operating room increases the chances of errors in coding, a burden that could be relieved by AI [100]. Researchers implemented an AI model to extract procedure codes from free-text surgical notes to provide a transparent and standardized coding validation solution. In over 3000 reviewed cases with over 8000 CPT codes, the model was able to identify errors in 12% of cases and outperformed surgeons in adherence to the referenced standard. Furthermore, prospective testing on 268 notes demonstrated effective and applicable real-world performance. Studies such as these demonstrate real-world applications for AI solutions that enhance providers' ability to deliver care while promoting cost-saving and efficiency. Complex urologic procedures involving layered CPT coding, such as microsurgical varicocelectomy with concurrent denervation or multicomponent penile prosthetic revision, are high-value targets for administrative agents that can reduce undercoding and generate payer-specific prior authorization documentation.

Research agents are autonomous systems that sense and interpret information from large datasets to support clinicians in care delivery, or in assisting scientific researchers in conducting effective and efficient research [20,101]. They can function as chatbots that support providers by leveraging patient data to explore external research relevant to care delivery, or as researchers themselves that adapt to biological insights, incorporate new scientific findings, and refine hypotheses as necessary in the research process [101,102]. As current research agents struggle to develop novel hypotheses, these agents mostly improve upon current research techniques by finding more refined parameters to improve upon baselines [103]. However, research agents have been used to understand the structure and function of protein sequences and genes, analyze phenotypes, and review clinical outcomes through the analysis of biomedical data [103-106]. Research agents could be a vital component of future health care establishments that aim to be driving scientific understanding while providing effective and relevant care. Open Evidence (OpenEvidence Inc.) is an example of a tool many providers are using to query high-quality clinical journals to identify answers to clinical questions. Incorporating such tools into an electronic medical record to be able to query sources of data based on individual patient information can provide real-time support for providers [81].

Current shortcomings in health care delivery necessitate innovation that can drive care delivery systems towards healthcare 5.0. AI is already being incorporated throughout different domains of health care to achieve this aim. Fahim et al describes AI-powered innovations ranging from personalized therapies that improve patient responsiveness and care plan compliance to smart-alerts that notify providers when critical levels appear in monitoring devices [107]. Alowais et al highlight AI’s capabilities to predict drug responses based on patient history or reduce administrative notetaking burdens of providers [15]. AI agents that incorporate these features could be the next step in improving accessibility and driving personalized, value-based care without compromising cost or quality of care. The agents described above could represent a future health care practice that effectively uses resources and available technology to meet the growing demands of a changing health care landscape.

To better describe the operations of AI agents, we describe the following conceptual framework. Patient health information remains at the center, protected by Health Insurance Portability and Accountability Act compliant and secure records barriers. Immediately surrounding protected health information are trusted, similarly secure, and minimally probability-based machine learning models that can recognize and categorize data for specialized purposes. Additional layers of security for protecting patient information stand between this categorized data output and Health Insurance Portability and Accountability Act compliant LLMs which communicate directly with the AI agents we describe. The agents are experts in processing data tailored to their specific function, are provider and patient facing, and require less security to access. Safety and security checks are present at all levels of iteration.

Future research should develop and rigorously test virtual-first urology care models that handle the majority of cases through AI-enabled telehealth, reserving brick-and-mortar encounters for only the most complex needs. Prospective studies should measure safety, patient outcomes, provider satisfaction, and cost-effectiveness compared to traditional episodic, in-person models [108,109]. However, issues intrinsic to machine learning models, like algorithmic bias and brittleness, present challenges to implementation currently. Furthermore, costs of implementing AI systems at scale, limited datasets, and privacy concerns are variables that must be addressed prior to implementation [110].

A critical limitation of this paper is that we discuss agentic AI as a unified concept without addressing condition-specific accuracy requirements. Different urologic conditions require different performance thresholds: low-acuity conditions (urinary tract infection screening) can tolerate higher error rates than high-acuity conditions (cancer staging). Rigorous implementation will require condition-specific validation protocols that are beyond the scope of this conceptual framework. Furthermore, performance thresholds must be influenced by risk-stratified deployment protocols tied to the consequence of clinical error. Areas that require human oversight must be clearly defined, and prospective studies must be validated in an applicable patient population. The limitations on our paper represent important areas for future prospective clinical research. Real-world implementation must depend on prospective validation, safety monitoring, bias assessment, regulatory compliance, and clinician oversight.

Scalable agentic systems rely on seamless data integration across electronic health records, wearables, remote monitoring devices, and AI modules. Future work must address data standardization, secure interoperability, and patient-controlled consent frameworks to enable continuous learning and context-aware decision-making while safeguarding privacy [108,109]. It is essential that AI agents ensure that correct critical information is passed without bias. Using Fast Healthcare Interoperability Resources standards in the initial development of AI agents will allow for effective and more seamless integration into current health records and clinical systems.

Successful transformation will depend on aligning AI agents with the daily workflows of providers and support staff. Implementation science should guide the design of interfaces, training, and human-AI comanagement protocols that minimize friction, reduce cognitive burden, and promote trust among both clinicians and patients [108,111,112].

While early demonstrations are promising, from triage accuracy to ambient scribing, large-scale, condition-specific validation is needed, especially in urology. Future research should further clarify how the AI agents described previously translate to measurable improvements in outcomes and health equity [108,112].

As AI systems assume more complex, autonomous functions, policy frameworks must evolve to clarify liability, algorithmic transparency, and ethical use. Research should inform adaptive regulations and reimbursement models that incentivize virtual-first practices while ensuring patient safety and equitable access [109]. In addition, legal input will be required for specific agents whose roles may have some degree of overlap with a licensed role, such as a registered nurse and triage, and in some cases, a health coach based on individual state statutes.

Especially during initial implementations, human involvement in key steps of the implementation of AI systems is necessary for long-term practical use [113]. Clinicians provide necessary guidance that can ensure reasonable systems are introduced, that regulate care delivery methods, and maintain quality of care over time [114]. For example, in a clinic looking to introduce agentic AI systems, clinicians could review actions proposed by the agent and check off on actions prior to execution. Actions performed autonomously by the agents can be monitored and flagged for review, allowing for quality monitoring over time. Practical human-in-the-loop frameworks such as this promote feasibility, improve provider trust in AI systems, and encourage patient safety.

Future work must prioritize inclusive training data and bias auditing to ensure AI agents perform equitably across diverse populations. Studies should test deployment in underserved communities, rural regions, and linguistically diverse groups, using community partnerships to design culturally competent agentic interactions [111].

The promise of Healthcare 5.0 depends on robust feedback loops. Future directions should include real-world monitoring, outcome auditing, and rapid iteration of AI models to adapt to evolving evidence, changing guidelines, and patient needs — ensuring that innovations remain clinically relevant and safe [109].