Chatbots can be programmed to pose targeted questions regarding patients’ symptoms, medical history, ocular examinations, and other factors that may relate to certain diseases. By assimilating this information, chatbots assist healthcare professionals in developing a comprehensive understanding of the patient’s condition, which can facilitate accurate diagnoses and informed treatment decisions.
3.3.2. Providing Clinical Guidelines and Reference Materials
Ophthalmology chatbots possess the capability to furnish healthcare professionals with clinical guidelines and reference materials. These chatbots can be programmed to access and retrieve information from reputable sources, such as medical databases, clinical practice guidelines, and research articles.
By having access to an extensive array of information, healthcare professionals can employ chatbots as swift references during patient consultations. Chatbots provide evidence-based recommendations for the diagnosis, treatment, and management of various ocular conditions. They also offer guidelines for monitoring and follow-up care, ensuring that healthcare professionals remain abreast of best practices in ophthalmology. Moreover, chatbots assist healthcare professionals in interpreting test results and imaging studies. They provide explanations for various ophthalmic tests, such as visual field testing, optical coherence tomography (OCT), or fundus photography.
Second, chatbots aid in standardizing practice and fostering consistency in care delivery. By providing guidelines and recommendations, chatbots assist healthcare professionals in adhering to established protocols and best practices. This fosters improved patient outcomes and enhances the quality of care across various healthcare settings.
3.3.3. Preparing Discharge Summaries and Operative Notes
Discharge summaries and operative notes are of importance in ophthalmology for maintaining continuity of care, facilitating effective communication among healthcare providers, serving as legal documentation, supporting research and education, and promoting patient safety and quality improvement. While the significance of these factors is recognized, variations in content and the time-consuming process pose the greatest challenges in achieving excellence
[30][31][32].
Using a chatbot for writing discharge summaries and operative notes can offer several advantages in terms of standardization, efficiency, accuracy, and convenience
[33]. With proper training and the improvement of AI libraries, chatbots can be seen as tools to assist ophthalmology healthcare in generating comprehensive and efficient discharge summary and operative notes. It is important to note that human review and validation are crucial to ensure accuracy, especially in complex cases and for handling situations that require clinical judgement and empathy.
3.3.4. Facilitating Appointment Scheduling and Reminders
Ophthalmology chatbots possess the capability to facilitate appointment scheduling and reminders for healthcare professionals. They can seamlessly integrate with existing scheduling systems and electronic health records, enabling patients to conveniently book appointments and receive timely reminders about their upcoming visits.
Chatbots provide patients with options for available appointment slots, assisting them in finding suitable times that align with their schedules. They also automatically send reminders to patients, reducing the likelihood of missed appointments and enhancing overall clinic efficiency. Patients can access the chatbot at their convenience, obviating the need for phone calls or waiting on hold to schedule appointments, thus enhancing patient satisfaction and engagement.
3.4. Ophthalmology Training
Chatbots hold immense potential as valuable educational tools in medical training, offering accessible and interactive resources to learners
[34]. In the context of ophthalmology training, chatbots can play a crucial role in various aspects, including providing the fundamentals of ophthalmology, facilitating case studies and diagnostic support, offering adaptive assessment, and even simulating surgical procedures. Furthermore, they can assist with administrative tasks and personalized course organization, tailoring the learning experience to individual needs. One of the most significant advantages of chatbots in this domain is their ability to provide comprehensive knowledge and reference materials essential for ophthalmology training.
Through incorporating simulated patient interactions, chatbots enable learners to practice and refine their clinical skills effectively. By presenting realistic case scenarios, students can engage in diagnostic decision-making and treatment proposals. The chatbot can then offer valuable feedback on their decisions, guiding them throughout the process. This feedback mechanism not only helps learners identify areas for improvement but also provides specific recommendations for additional study or practice, which can be invaluable for their professional growth.
4. Availability and Performance of Current Ophthalmology Chatbots
4.1. Chatbot Performance in Triage Ophthalmology Conditions
In a study performed by Tsui, J.C. et al., ten prompts reflecting common patient complaints related to common ophthalmology conditions were used to determine the suitability of ChatGPT 3.0 responses. The study also evaluated the precision of the responses by comparing the responses to the same questions from three individual chats. The study found a majority of responses were precise and suitable; however, 20% of responses were considered imprecise or unsuitable
[35].
4.2. ChatGPT Performance in Patient Education and Information Provision
A study from Potapenko et al. assessed the accuracy of patient information for five common retinal diseases (i.e., age-related macular degeneration, diabetic retinopathy, retinal vein occlusion, retinal artery occlusion, and central serous chorioretinopathy) using ChatGPT 3.0. They evaluated accuracies in disease summary, prevention, treatment options, and prognosis. Most responses showed high accuracy, with median ratings ranging from “good/only minor non-harmful inaccuracies” to “very good/no inaccuracies.” However, treatment options had “moderate/potentially misinterpretable inaccuracies”, with 12 of 100 treatment responses showing “potentially harmful inaccuracies”
[7].
4.3. Chatbots Examples in Supporting for Healthcare Professionals
A preliminary work utilizing ChatGPT 3.0 to generate discharge summaries across subspecialties found that the AI-constructed documents were able to shorten the processes; however, their quality was based on the completeness of the prompts given and required training and adjustment
[33].
GlauCUTU is another example of a chatbot designed to aid with glaucoma diagnosis. This chatbot utilizes a deep learning algorithm to provides real-time response to help in screening glaucoma based on optic disc photo
[36]. GlauCUTU operates on the mobile and desktop social messaging service LINE (
Figure 8). With the integration of a messaging application, it provides a convenient and readily accessible mode of communication and can be considered an example of an ophthalmologist virtual assistant
[36].
Figure 8. Example of responses generated by GlauCUTU illustrating glaucoma risk assessment from an optic disc photo.
4.4. Chatbot Performance in Ophthalmology Knowledge Assessment
The performance of chatbots can vary across disciplines and different subspecialties. While ChatGPT answered a majority of general medicine licensing examination questions correctly
[37], the present version of ChatGPT did not correctly answer multiple-choice questions (MCQ) for the US board certification preparation (i.e., Ophthalmic Knowledge Assessment Program (OKAP) and Written Qualifying Exam (WQE) from the OphthoQuestions) to a desirable level. A study indicated that ChatGPT 3.0 correctly answered only 46% of 125 multiple-choice questions intended to prepare for board certification examinations
[38] Figure 9.
Figure 9. Performance of an artificial intelligence chatbot in ophthalmic knowledge assessment.
5. Challenges and Future Directions in Ophthalmology Chatbots
5.1. Ethical and Legal Considerations
5.1.1. Privacy and Data Security
Privacy and data security are critical concerns in the context of ophthalmology chatbots, particularly when considering the diverse legal frameworks across different nations. These chatbots gather and process sensitive patient information and personal identifiers, necessitating robust security measures to safeguard these data from unauthorized access, breaches, or misuse.
Ensuring adherence to industry standards and best practices for data encryption and storage is fundamental. Employing encryption techniques, such as secure socket layer (SSL) encryption, can protect the transmission of data. However, developers must also navigate the complexities of varying national laws, which often include additional rules in the field of transmissions, servers, administration, and telecommunication standards. This necessitates a flexible approach to compliance, ensuring that chatbots meet the specific legal requirements of each jurisdiction in which they operate.
Healthcare organizations and developers should establish explicit protocols for data access and sharing, considering different legal landscapes. Transparency in data handling practices is crucial to foster trust among patients, healthcare professionals, and chatbot providers.
5.1.2. Informed Consent and Confidentiality
Obtaining informed consent and ensuring confidentiality are pivotal ethical considerations when utilizing ophthalmology chatbots. Patients should be fully informed about the purpose, capabilities, and limitations of the chatbot, as well as the type of data it collects and how that data will be used. Informed consent should be sought before engaging patients in chatbot interactions and data collection.
Confidentiality is equally crucial in maintaining patient trust and complying with ethical and legal standards. Ophthalmology chatbots must adhere to stringent confidentiality protocols to ensure that patient data are accessible only to authorized individuals involved in healthcare provision. Measures such as encryption, secure data transmission, and restricted data access help preserve the confidentiality of patient information.
5.1.3. Compliance with Regulatory Standards
Compliance with regulatory standards is essential for ophthalmology chatbots, to ensure patient safety, quality of care, and legal compliance. These chatbots must adhere to relevant regulations and guidelines, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union.
Thorough assessments should be conducted by healthcare organizations and developers of ophthalmology chatbots to ensure compliance with these regulations. This involves reviewing and aligning data handling practices, security measures, and consent procedures with the requirements stipulated in the regulations. Regular audits and assessments can identify any gaps in compliance and facilitate necessary adjustments.
5.2. Integration with Existing Healthcare Systems
5.2.1. Interoperability and Integration Challenges
Ophthalmology chatbots require seamless interoperability and integration with diverse healthcare systems, including electronic medical records, diagnostic devices, and telehealth systems, for efficient usage and effective usage.
One of the primary challenges lies in the diversity of existing healthcare systems, each with its own compatibility level. In addition, diagnosis in the field of ophthalmology frequently requires the integration of multimodality instruments, such as tonometry, perimetry, fundus photography, and optical coherence tomography. Ophthalmology chatbots need to be designed in a way that enables communication and data exchange with various software applications and databases. This necessitates adherence to standardized data formats and protocols that facilitate smooth interoperability.
Ophthalmology chatbots should also be compatible with a variety of devices and operating systems (e.g., desktops, mobile devices). Compatibility across a wide range of devices ensures accessibility and usability for healthcare professionals in clinics, hospitals, and remote sites.
5.2.2. Collaboration with Electronic Health Records
Collaboration with electronic health records (EHR) that contain comprehensive patient information allows chatbots to access and update information in real time. This enhances their capacity to provide personalized and accurate care. However, challenges arise when it comes to EHR integration. Different healthcare organizations may utilize diverse EHR systems, each characterized by a unique data structure and interface. This variability poses a challenge in developing chatbots capable of seamlessly interacting with a wide range of EHR systems.
One potential solution is the development of standardized data exchange formats such as fast healthcare interoperability resources (FHIR), which promote interoperability between EHRs and chatbots. FHIR facilitates the exchange of structured health data, enabling chatbots to retrieve and update patient information from EHR systems in a standardized and consistent manner.
5.2.3. Seamless Communication with Healthcare Providers
Effective care requires seamless communication between ophthalmology chatbots and healthcare providers. Chatbots should facilitate easy and efficient information exchange, enabling healthcare professionals to review patient data, provide guidance, and make well-informed decisions.
One challenge in achieving seamless communication lies in presenting information in a format that is easily comprehensible and actionable for healthcare providers. Chatbots should present patient data and clinical recommendations concisely and in an organized manner, allowing healthcare providers to quickly grasp the relevant information. The incorporation of NLP capabilities can assist in understanding and presenting complex medical information.
Ensuring the security and privacy of communications is also of utmost importance. Chatbot systems should employ secure communication channels and encryption techniques to safeguard sensitive patient information during interactions with healthcare providers. Compliance with relevant privacy regulations, such as HIPAA, is essential in upholding patient confidentiality and meeting legal requirements.
5.3. Advancements and Future Innovations
5.3.1. Artificial Intelligence and Machine Learning in Chatbots
AI and ML are driving advancements in ophthalmology chatbots, enabling them to learn and improve from interactions with patients and healthcare providers. This leads to more accurate and effective diagnostic capabilities (
Figure 10).
Figure 10. Future studies in the utilization of ophthalmology chatbots.
5.3.2. Multilingual and Cross-Cultural Adaptation
Multilingual and cross-cultural adaptation is a significant advancement in ophthalmology chatbots, particularly in the context of global healthcare, where language and cultural diversity are prevalent. Chatbots that can effectively communicate and interact with patients from different linguistic and cultural backgrounds improve access to care and enhance patient satisfaction.