Abstract

ChatGPT is forging a revolution in the realm of human-computer interaction, establishing new paradigms for what artificial intelligence can achieve. Also known as the Generative Pretrained Transformer (GPT), ChatGPT represents a groundbreaking evolution in AI that possesses the ability to generate human-like text. Emerging as a potent asset in various fields, including healthcare, ChatGPT holds substantial transformative potential.

This document seeks to provide an extensive exploration of ChatGPT, its functionalities and its implications in the healthcare sector. It scrutinizes the evolution of ChatGPT, the architectural foundation it is built upon and the methodologies employed in its training. The document further explores the applications of ChatGPT in healthcare, emphasizing its role in diagnosis, treatment formulation, patient communication, decision support and spearheading research advancements.

Moreover, the document tackles the challenges and risks related to the integration of ChatGPT in healthcare, such as concerns about data privacy, potential biases and ethical deliberations. Finally, it discusses the prospects and future trajectories of ChatGPT in revolutionizing healthcare delivery, enhancing patient outcomes and promoting medical knowledge.

By offering an extensive understanding of ChatGPT, this document serves as a beneficial reference for researchers, healthcare professionals and policymakers aiming to delve into the potential of this technology in healthcare. Overall, this document meticulously outlines ChatGPT’s capacity to transform healthcare and advocates for further exploration and assimilation of AI technologies to propel the field forward.

Keywords: ChatGPT; Artificial Intelligence; NLP; Management; Healthcare Engagement; Innovation

Introduction

ChatGPT signifies a pioneering advancement in the sphere of Artificial Intelligence (AI) and Natural Language Processing (NLP). This document offers a thorough exploration of ChatGPT, delving into its functionalities, potential applications and the ripple effects it has across numerous domains, especially the healthcare industry. Today, ChatGPT, a brainchild of OpenAI, is rooted in the GPT-3.5 architecture and trained on a vast array of datasets. This extensive training enables it to generate text responses that mirror human conversation. With its remarkable abilities in understanding and generating language, it has become an essential instrument for a diverse set of sophisticated applications, including those in healthcare.

Highlighting the transformative potential of ChatGPT in healthcare, it’s worth examining its capacity to bolster healthcare professionals and researchers in vital areas like information retrieval, decision-making support and personalized patient care. Leveraging its enormous knowledge base, ChatGPT is capable of efficiently retrieving and summarizing medical literature, research studies and clinical guidelines, offering healthcare practitioners immediate access to accurate information. Moreover, ChatGPT’s superior NLP abilities allow it to scrutinize complex medical data, thereby aiding in clinical decision-making. It can assist in medical diagnostics, suggest suitable treatment approaches and predict patient outcomes based on existing data. Its proficiency in comprehending and generating natural language responses facilitates smooth communication among healthcare professionals, patients and medical systems. The document meticulously addresses the ethical implications surrounding the use of AI in healthcare. It probes issues such as privacy, data security, mitigating bias and the necessity of preserving human supervision in the decision-making procedure. The potential risks and hurdles associated with introducing ChatGPT into healthcare settings are also considered, underscoring the need for accountable and transparent AI application. Real-world scenarios and case studies exemplify the practical applications of ChatGPT in healthcare. These encompass intelligent virtual assistants for patient engagement and education, automated medical triage systems and chatbot-powered telemedicine platforms. The document accentuates successful ChatGPT deployments, showcasing its positive influence on healthcare delivery, efficiency and patient outcomes. Lastly, the document delves into future trajectories and potential areas for further research and development of ChatGPT in healthcare. It examines the possibility of integrating additional data sources, such as electronic health records and wearable devices, to boost the model’s predictive capacities and enable more personalized and proactive healthcare interventions. ChatGPT: Evolution, architecture and training methodologies ChatGPT, standing for Generative Pretrained Transformer, marks an important juncture in the progression of Artificial Intelligence (AI) and Natural Language Processing (NLP). OpenAI’s brainchild, this advanced language model has reshaped how machines understand and generate human-like text. To fully grasp the potential and prowess of ChatGPT, one must first delve into its evolutionary journey and the architectural design that powers it [1,2]. Tracing the roots of ChatGPT leads us back to the original GPT model, the progenitor of generative language models. GPT burst onto the scene as an NLP revelation, harnessing the power of a transformer architecture, famed for capturing long-range dependencies in text. By training on extensive volumes of text data, GPT acquired the ability to generate coherent and contextually apt sentences, exhibiting a remarkable understanding of language. Bolstered by the success of GPT, ChatGPT emerged as a significant stride in conversational AI. It strives to enable dynamic and interactive exchanges, making the model a conversational partner for users. This progression called for an adaptation in architecture and training methodology to foster a more responsive and engaging conversational experience [5]. ChatGPT’s architecture hinges on a deep neural network known as a transformer. Transformers have taken the NLP realm by storm, surpassing traditional Recurrent Neural Networks’ (RNNs) limitations and capturing contextual information more efficiently. Self- attention mechanisms within the transformer architecture enable the model to measure the significance of different words within a sentence, facilitating improved accuracy in text understanding [6,7]. ChatGPT’s training follows a two-step process. The initial stage involves pretraining on a vast corpus of publicly available text from the internet, absorbing a broad spectrum of linguistic patterns, grammar and context. During pretraining, the model learns to predict the succeeding word in a sentence given the previous context, thereby acquiring a robust understanding of language patterns [8]. The second stage, known as fine-tuning, entails training the model on more tailored and curated datasets with the aid of human reviewers. These reviewers, following OpenAI’s guidelines, review and rate possible model outputs, establishing a feedback loop that refines and improves the model’s responses. This iterative mechanism ensures that ChatGPT is in sync with human values and produces coherent and suitable responses. The progression of ChatGPT’s architecture and training methodologies has sparked significant advancements in its language generation abilities. It can now participate in meaningful conversations, provide detailed explanations, answer queries and offer a wide array of responses across diverse topics. However, it’s crucial to acknowledge that ChatGPT has limitations and may occasionally produce incorrect or nonsensical responses due to the inherent challenges of training a language model on such a large scale. Efforts to address these limitations are part of the ongoing evolution of ChatGPT’s architecture, aiming for improved performance and enhanced user experiences. Techniques such as reinforcement learning, active learning and model distillation are actively being explored by researchers and engineers to further refine and optimize ChatGPT’s architecture and training process. The transformer architecture has greatly advanced the field of AI by overcoming the limitations of traditional Recurrent Neural Networks (RNNs) and capturing long-range dependencies in text more effectively. A solid understanding of the underlying architecture and training methodologies is key to comprehending ChatGPT’s remarkable language generation abilities [7-9]. Constructed from multiple layers of self-attention mechanisms and feed-forward neural networks, the transformer architecture allows each layer to operate independently and process information in parallel, facilitating efficient computation. The self-attention mechanism enables ChatGPT to weigh the significance of different words in a sentence, considering their relationships, allowing for accurate language understanding and generation. ChatGPT’s training process is a two-pronged approach: pretraining and fine-tuning. During pretraining, the model is exposed to an immense amount of publicly available text from the internet. The objective is to enable the model to learn the statistical properties of language, absorbing a strong understanding of grammar, syntax and semantics. During pretraining, ChatGPT predicts the next word in a sentence based on the preceding context, learning to generate coherent and contextually relevant text. Upon completion of pretraining, the model undergoes a fine-tuning phase. Fine-tuning involves training the model on more specific datasets using a technique called supervised learning. Human reviewers, following OpenAI’s guidelines, review and rate possible model outputs. This feedback loop helps improve the model’s responses and aligns them with human values, ensuring ChatGPT generates more appropriate and reliable text. The iterative process of fine-tuning with human reviewers is essential for honing ChatGPT’s performance. It helps to address potential biases, improve response quality and reduce instances of generating inappropriate or misleading information. OpenAI maintains an ongoing relationship with the innovative entity known as ChatGPT, short for Generative Pretrained Transformer, marks a crucial leap in the progress of Artificial Intelligence (AI) and Natural Language Processing (NLP). Developed by OpenAI, this cutting-edge language model has transformed how machines interpret and generate text that mirrors human interaction. It’s key to recognize the evolution and structural framework of ChatGPT to fully comprehend its capabilities and potential [1,5,10]. ChatGPT’s roots can be traced back to the original GPT model, the pioneer of generative language models. GPT represented a significant leap forward in NLP, utilizing a transformer architecture celebrated for its ability to capture text’s long-range dependencies. Trained on vast amounts of textual data, GPT developed the ability to generate sentences that were coherent and pertinent to the context, showcasing an extraordinary comprehension of language. ChatGPT, building on GPT’s success, symbolizes a significant advancement in the realm of conversational AI. It’s designed to foster dynamic and interactive exchanges, allowing users to engage in conversation with the model. This progression necessitated adjustments to the architecture and training approach, facilitating a more responsive and engaging conversational experience [5]. The structural framework of ChatGPT is built on a deep neural network, known as a transformer. Transformers have dramatically changed the NLP landscape, surpassed the constraints of traditional Recurrent Neural Networks (RNNs) and achieved superior contextual information capture. Within the transformer architecture, self-attention mechanisms enable the model to ascertain the relevance of different words within a sentence, thereby enhancing its understanding and generation of text [6,11]. ChatGPT follows a two-step training procedure. The first step involves pretraining on a vast collection of publicly available text from the internet, which allows it to grasp a wide array of linguistic patterns, grammar and context. During this pretraining, the model learns to predict the subsequent word in a sentence given the prior context, thereby gaining a robust understanding of language patterns [12]. The second step, known as fine-tuning, involves training the model on more specialized and curated datasets with the assistance of human reviewers. These reviewers, adhering to guidelines provided by OpenAI, review and rate potential model outputs. This creates a feedback loop that helps refine and enhance the model’s responses. This iterative procedure ensures that ChatGPT aligns with human values and produces appropriate and coherent responses. The evolution of ChatGPT’s architecture and training methodologies have led to significant enhancements in its language generation capabilities. It can now engage in meaningful dialogues, provide in-depth explanations, answer queries and offer a wide range of responses across various topics. However, it is important to acknowledge that ChatGPT has limitations and can occasionally generate incorrect or nonsensical responses due to the inherent complexities of training a language model at such a scale. The continued evolution of ChatGPT’s architecture aims to address these limitations, with the goal of improving performance and enriching user experiences. Techniques such as reinforcement learning, active learning and model distillation are being actively researched and implemented by engineers and researchers to further refine and optimize ChatGPT’s architecture and training process. The transformer architecture has fundamentally altered the field of AI by overcoming the limitations of traditional Recurrent Neural Networks (RNNs) and effectively capturing long- range dependencies in text. An understanding of the underlying architecture and training methodologies is integral to grasping how ChatGPT achieves its impressive language generation capabilities [5,7,9]. Comprising multiple layers of self-attention mechanisms and feed-forward neural networks, the transformer architecture enables each layer to operate independently and process information in parallel, promoting efficient and parallelizable computation.

The self-attention mechanism allows ChatGPT to determine the importance of different words in a sentence, considering                their relationships, thus ensuring more accurate language understanding and generation. ChatGPT’s training process comprises two main steps: pretraining and fine-tuning. In the pretraining phase, the model is fed a massive amount of publicly available text from the internet. The aim of pretraining is to enable the model to learn the statistical properties of language and gain a solid understanding of grammar, syntax and semantics. During pretraining, ChatGPT predicts the next word in a sentence given the preceding context, learning to generate coherent and contextually relevant text.

Post-pretraining, the model undergoes a fine-tuning phase. Fine-tuning involves training the model on more specific datasets using a supervised learning technique. In this phase, human reviewers, adhering to guidelines provided by OpenAI, review and rate potential model outputs. This feedback loop helps improve the model’s responses and align them with human values, ensuring that ChatGPT generates more suitable andreliable text. The iterative process of fine-tuning with human reviewers plays a crucial role in enhancing ChatGPT’s performance. It assists in mitigating potential biases, improving response quality and reducing instances of generating inappropriate or misleading information. OpenAI maintains a continuous relationship with the reviewers, offering clarifications, addressing concerns and persistently refining the guidelines to improve the model’s performance and responsiveness. It’s crucial to recognize that ChatGPT’s training methodologies are designed to balance the benefits of open-ended language generation with the need to prevent the generation of misleading or harmful content. However, the challenges of training at such an extensive scale mean that ChatGPT may occasionally produce incorrect or nonsensical responses. OpenAI acknowledges these limitations and is actively working to address them through ongoing research and development. In conclusion, the underlying architecture of ChatGPT is based on the transformer model, enabling it to effectively capture contextual dependencies in text. The training process involves pretraining on a vast corpus of internet text, followed by fine-tuning with human reviewers to align the model’s responses with human values. This blend of architecture and training methodologies enables ChatGPT to generate coherent and contextually relevant text, marking it as a potent tool for various language-related tasks.

Applications of ChatGPT in Healthcare (Patient Journey)

Diagnosis and Triage

In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) is revolutionizing traditional processes, enabling increased accessibility, efficiency and accuracy. Among the various AI models, ChatGPT, developed by OpenAI, is demonstrating significant potential in enhancing patient journeys, particularly in the realms of diagnosis and triage.

Diagnosis forms the crucial first step in the patient journey. Accurate and timely identification of a condition lays the foundation for subsequent treatment planning and care. ChatGPT, with its ability to understand and generate human-like text, is poised to be a significant ally in this process. Through interaction with patients, the AI model can gather extensive information about symptoms, medical history, lifestyle factors and more. Its extensive training on a vast corpus of data enables it to discern patterns and correlations that may be less obvious to the human eye, thereby assisting healthcare professionals in formulating a precise diagnosis.

Furthermore, ChatGPT’s potential extends to the field of medical imaging, where it can be used to identify anomalies in radiographic images such as X-rays, CT scans and MRI scans. By training the model on a multitude of medical images annotated with findings, ChatGPT can learn to detect and interpret these images, aiding in the diagnostic process. While it is essential to note that AI is not a substitute for a medical professional’s expertise, it can serve as a powerful tool in augmenting their diagnostic acumen. Beyond diagnostics, ChatGPT also holds promise in the area of patient triage – the process of determining the priority of patients’ treatments based on the severity of their condition. In emergency healthcare settings, accurate and timely triage is vital to ensure that patients receive the care they need when they need it. ChatGPT, with its language comprehension and generation abilities, can interact with patients or caregivers to gather preliminary information about the patient’s condition. By correlating this information with its extensive training data, ChatGPT can suggest a preliminary triage level to healthcare professionals, enabling them to make more informed decisions about patient care. Moreover, in non-emergency settings, ChatGPT can assist in prioritizing outpatient consultations or telemedicine sessions based on the urgency and severity of the reported symptoms. In this way, it can help streamline the healthcare process, reducing waiting times and ensuring efficient allocation of healthcare resources. The integration of ChatGPT into the healthcare system is also expected to enhance patient engagement and education. By providing patients with reliable and comprehensible health information, ChatGPT can foster improved understanding of their condition and its management, thereby promoting better adherence to treatment plans and, consequently, improved health outcomes. Despite these potential benefits, it is important to acknowledge the ethical and practical considerations associated with the application of AI in healthcare [13-15]. Ensuring data privacy, managing potential biases in the AI model and maintaining the irreplaceable value of human touch in patient care are paramount considerations. It’s also critical to remember that while AI, including ChatGPT, can aid and augment healthcare delivery, it is not intended to replace the crucial role of healthcare professionals. In conclusion, the application of ChatGPT in the patient journey, particularly in diagnosis and triage, holds significant potential to enhance the efficiency, accuracy and overall quality of healthcare delivery. As we continue to navigate the frontiers of AI in healthcare, it is vital to approach this exciting progress with careful consideration, always prioritizing the ultimate goal of improving patient care and outcomes.

Treatment Planning and Decision Support

The incorporation of Artificial Intelligence (AI) in the healthcare sector has brought about a profound transformation in the delivery and experience of healthcare services. Notably, ChatGPT, developed by OpenAI, is heralding significant advancements in the journey of patient care, particularly in the realms of treatment planning and decision support.

Treatment planning, a core component of the patient journey, involves designing a structured approach to manage and alleviate the health condition identified in the diagnostic phase. ChatGPT, with its advanced language generation capabilities and deep understanding of medical literature, can assist healthcare professionals in this crucial step. By analyzing patient information, understanding the intricacies of their medical history and referencing the latest clinical guidelines and research findings, ChatGPT can suggest potential treatment pathways to the treating physician. For instance, for a patient diagnosed with hypertension, ChatGPT can aid in designing a treatment plan that encompasses dietary modifications, physical activity recommendations and appropriate medication. By integrating the patient’s lifestyle factors, the AI model can suggest personalized modifications, contributing to a more comprehensive and tailored treatment strategy. In scenarios involving complex multi-drug regimens, ChatGPT can also help identify potential drug-drug interactions and suggest alternatives, ensuring the safety and efficacy of the treatment. Beyond treatment planning, ChatGPT is also demonstrating significant potential in the sphere of decision support, a critical aspect that guides healthcare professionals in the dynamic environment of patient care. Medical decision-making often involves sifting through a vast amount of information under time constraints, which can be challenging [15-18].

ChatGPT can alleviate this burden by quickly synthesizing relevant data, presenting key insights and suggesting potential courses of action based on the available evidence. In complex cases involving multiple comorbidities, ChatGPT can assist in evaluating the potential risks and benefits of various treatment options, facilitating informed clinical decision-making. Similarly, in situations where patients’ symptoms do not align with conventional diagnostic patterns, ChatGPT can bring forward lesser-known conditions or rare diseases that may align with the clinical picture, ensuring a broader perspective in the decision-making process.

Furthermore, ChatGPT’s conversational AI capabilities enable it to engage in discussions with healthcare professionals, providing detailed explanations for its suggestions and adapting its recommendations based on feedback. These interactive dynamic fosters an environment of collaborative decision-making, combining the strengths of both AI and human expertise. However, while these applications of ChatGPT in treatment planning and decision support demonstrate exciting potential, it’s essential to address their implementation cautiously. It’s critical to ensure the model’s suggestions align with ethical standards and that patients’ data privacy and security are safeguarded. Equally important is the recognition that AI should augment, not replace, human judgment and the unique human touch in healthcare. In conclusion, ChatGPT’s application in the patient journey, particularly in treatment planning and decision support, presents a promising frontier in the delivery of patient- centered, efficient and evidence-based healthcare. As we continue to explore and harness the potential of AI in healthcare, the focus should always remain on enhancing patient care and outcomes, while ensuring ethical integrity and data security.

Patient Engagement and Education

ChatGPT, in its role within healthcare, has demonstrated invaluable utility, particularly in the realm of patient engagement and education. Its proficiency in generating human-like text and maintaining interactive conversations permits it to augment patient engagement, enabling individuals to actively participate in their health management and offering reliable health-related information.

ChatGPT can function as a virtual health companion, extending personalized guidance and assistance to patients. Through its interactive, conversational interface, ChatGPT can resolve patient queries, offer information regarding symptoms, treatment plans and prescribed medications and disseminate general health knowledge [19-24]. This facilitates patient access to pertinent information and fosters active patient involvement in their healthcare journey. Moreover, ChatGPT has the capability to simplify intricate medical terminology for patients, contributing to their understanding of health conditions, treatment strategies, preventive steps and practical advice for healthy living. By offering information through a dialogue-based format, ChatGPT enhances patient understanding, empowering them to make informed decisions concerning their health [4].

For chronic condition management, ChatGPT can provide reminders for medication adherence, advocate healthy lifestyle modifications and give tips for self-care. Regular interactions with ChatGPT can cultivate a supportive environment and instill accountability, motivating patients to be proactive in their health management and encouraging healthful behaviors. ChatGPT can also be a conduit for large-scale health information dissemination. By integrating ChatGPT into patient portals or healthcare websites, instant access to reliable health knowledge can be provided. This enables individuals to resolve health-related queries, alleviate common concerns and access educational resources at their convenience [3].

However, it is vital to understand that ChatGPT is not a replacement for the expertise and guidance of healthcare professionals. Its role is to supplement and enhance patient engagement and education, not to substitute personalized medical consultation. Therefore, patients should always be advised to seek guidance from their healthcare providers for specific medical concerns and personalized treatment options.

In conclusion, ChatGPT presents a host of applications centered around patient engagement and care, as detailed by Javaid, et al. and Mbakwe AB, et al., [23,22]. These encompass:

• Educating patients in an accessible manner
• Assisting in clinical studies
• Monitoring patients remotely
• Providing easy access to pertinent health information
• Supplying preliminary medical suggestions
• Scheduling appointments efficiently
• Identifying and interpreting patient symptoms
• Reminding patients about their medications
• Developing a patient-centric approach
• Translating complex medical terminology
• Serving as a digital assistant for doctors
• Enhancing communication within the healthcare realm
• Providing rapid access to medical data
• Offering individualized health advice

Keeping patients informed, engaged and educated in their healthcare journey. ChatGPT’s ability to offer personalized advice, answer patients’ queries and provide reliable health-related information can empower individuals to take an active role in their health management. This can enhance health literacy and promote positive health outcomes. When used in conjunction with professional medical guidance, ChatGPT stands to significantly augment patient engagement, educational experiences and overall healthcare experiences. Research advancements and knowledge generation ChatGPT boasts numerous applications in the advancement of research and knowledge generation in the healthcare field. Its ability to generate human-like text and process a wide scope of medical literature allows it to assist researchers, scientists and healthcare professionals in manifold ways, fostering progress in medical understanding and sparking innovative discoveries. A primary application of ChatGPT lies in its ability to facilitate literature reviews and knowledge synthesis. Leveraging its comprehension and analysis of extensive medical texts, ChatGPT can support researchers by scanning broad literature databases, pinpointing pertinent studies and distilling crucial information. This can expedite the research process significantly and assist researchers in keeping abreast of the newest advancements in their respective areas [25-27]. Moreover, ChatGPT can aid in the generation of hypotheses and the design of experiments. Through interactive dialogues with researchers, ChatGPT can assist in ideation, encourage exploration of diverse perspectives and help generate innovative research hypotheses. It can also offer insights into potential experimental methodologies, sample sizes and statistical considerations, thereby aiding researchers in crafting robust and well-structured studies [28-32].

ChatGPT’s capacity to process and understand voluminous datasets makes it a valuable tool in data analysis and interpretation. It can assist researchers in identifying patterns, trends and correlations that might otherwise be overlooked. By providing preliminary analyses, statistical overviews and visualizations, ChatGPT enables researchers to extract meaningful insights and draw conclusions grounded in evidence. Furthermore, ChatGPT can facilitate collaboration and knowledge exchange among researchers. It can enable discussions, virtual meetings and platforms for knowledge sharing, where researchers can disseminate their findings, seek feedback and collaborate on interdisciplinary projects. By tapping into the collective intelligence and diverse expertise of researchers, ChatGPT can spur the generation of innovative ideas and expedite the pace of discoveries. However, while ChatGPT can aid in advancing research and knowledge generation, it should be seen as a complementary tool to human expertise, not a replacement. It’s vital for researchers to critically assess the outputs generated by ChatGPT, validate findings through rigorous scientific procedures and use their domain knowledge to make informed decisions. In conclusion, ChatGPT offers considerable applications in promoting research and knowledge generation within the healthcare field. By assisting with literature reviews, hypothesis generation, experimental design, data analysis and collaboration, ChatGPT can help accelerate the pace of research, stimulate innovation and drive scientific breakthroughs. When used in tandem with human expertise, ChatGPT holds the potential to transform the research landscape and facilitate significant advancements in healthcare knowledge. Advantages and Limitations of ChatGPT in Healthcare.

Advantages and Benefits

The predominant advantages and merits of utilizing ChatGPT in the healthcare sector include [33-35]:

• Access to a wealth of medical knowledge: ChatGPT can draw from a vast pool of medical literature and resources, allowing it to provide healthcare professionals and patients with contemporary, evidence-backed information. This can bolster accurate decision-making, promote patient education and enable informed dialogue
• Customized interaction: ChatGPT’s dialogue-based interface facilitates bespoke interactions with users. It can tailor its responses to individual requirements, preferences and medical histories, thereby creating a more engaging and personalized experience. This customization can enhance patient engagement, bolster comprehension and foster adherence to treatment regimens
• Efficiency of time and resources: ChatGPT can conserve healthcare professionals’ time by aiding with tasks such as literature review, data analysis and the generation of prospective treatment plans. It can rapidly sift through extensive volumes of information and deliver pertinent insights, enabling clinicians to concentrate on vital decision-making and patient care
• Scalability and accessibility: As a virtual assistant, ChatGPT is accessible anytime, anywhere, provided there’s an internet connection. This constant availability makes it a crucial resource for patients in need of immediate health information and for healthcare professionals seeking support outside conventional working hours. Comprehension and generation of language: ChatGPT’s capacity to comprehend complex language and create human-like responses enhances communication between patients and healthcare providers. Its ability to understand nuanced queries and offer detailed explanations can lead to improved patient satisfaction and clarity in healthcare interactions
• Support for decision-making and collaboration: ChatGPT can function as a decision-support tool, offering insights and recommendations to healthcare professionals. It can assist in planning treatments, diagnosing conditions and making triage decisions, thus aiding in more informed and efficient decision-making processes. In addition, it can promote collaboration among healthcare providers by offering a platform for knowledge exchange and interdisciplinary discussions. 516. The principal constraints of employing ChatGPT in Healthcare encompass
• Limited understanding of context: ChatGPT might encounter difficulties in comprehending context-specific details or subtleties within healthcare dialogues. There’s a risk it could misinterpret or offer inaccurate responses if the context isn’t communicated           This       highlights            the          importance          of            healthcare professionals carefully validating and interpreting the outputs generated by ChatGPT
• Ethical implications: As ChatGPT is dependent on the data it’s been trained with; it could introduce biases or inaccuracies if the training data isn’t diverse or representative enough. Healthcare professionals must be aware of possible biases and ensure that any decisions based on ChatGPT’s outputs are critically evaluated to prevent unintended outcomes or disparities in patient care
• Absence of physical examination capabilities: As ChatGPT operates primarily through text-based interactions, its capability to conduct physical examinations or evaluate vital signs is limited. It can’t supplant the need for hands-on clinical assessments and diagnostic tests, which remain crucial components of precise healthcare evaluations
• Risk of misinterpretation: The responses generated by ChatGPT are based on patterns in the training data, which could result in responses that seem logical but aren’t clinically accurate or appropriate. It’s crucial for healthcare professionals to exercise caution and use their expertise to appropriately validate and interpret ChatGPT’s output
• Concerns about data privacy and security: Interactions with ChatGPT may entail sharing sensitive patient information. Healthcare organizations must ensure strong security measures to safeguard patient privacy and adhere to relevant data protection regulations

In summary, while ChatGPT offers numerous benefits in healthcare, such as access to medical knowledge, personalized interactions, time efficiency, scalability, language comprehension and decision support, its limitations, including lack of contextual understanding, potential biases, dependence on training data, inability to conduct physical examinations, potential for misinterpretation and data privacy concerns, must also be acknowledged. By capitalizing on the benefits while being mindful of the limitations, healthcare professionals can effectively and responsibly exploit the potential of ChatGPT, thereby enhancing patient care and outcomes.

Limitations and Challenges

The key constraints and hurdles of utilizing ChatGPT in Healthcare, as highlighted by Kohane IS, Krawczyk M, et al., Char DS, et al., are [36-43]:

• Incomplete contextual understanding: ChatGPT can sometimes find it difficult to interpret context-specific details or subtle nuances within healthcare discussions. There’s a risk of misinterpretation or provision of inaccurate responses if the context isn’t adequately explained. This underlines the importance of healthcare professionals rigorously validating and interpreting ChatGPT’s responses
• Ethical issues: ChatGPT is dependent on the data it’s been trained with, which can lead to biases or inaccuracies if the training data isn’t diverse or representative. It’s essential for healthcare professionals to be aware of potential biases and ensure that decisions made based on ChatGPT’s outputs are critically assessed to prevent unintended consequences or disparities in patient care
• Inability to conduct physical examinations: ChatGPT’s functionality is primarily text-based, which restricts its capacity to perform physical examinations or evaluate vital signs. It can’t supplant the significance of in-person clinical assessments and diagnostic tests, which are crucial for precise healthcare evaluations
• Risk of misinterpretation: The responses generated by ChatGPT are based on patterns in the training data, which could result in responses that seem reasonable but aren’t clinically accurate or suitable. Healthcare professionals need to be cautious and apply their judgement to validate and interpret ChatGPT’s outputs correctly
• Data privacy and security issues: Interactions with ChatGPT might necessitate sharing sensitive patient information. Healthcare organizations must put robust security measures in place to safeguard patient privacy and adhere to relevant data protection regulations
• Requirement for continuous training and improvement: The efficacy of ChatGPT can be influenced by its training data and the algorithms used. It’s necessary to undertake ongoing training and improvements to rectify biases, enhance accuracy and stay in line with evolving medical knowledge. In summary, while ChatGPT provides substantial benefits in healthcare, such as access to medical knowledge, personalized interactions, time efficiency, scalability, language comprehension and decision support, it also presents a range of limitations and challenges. These include incomplete contextual understanding, ethical issues, inability to conduct physical examinations, potential misinterpretation, data privacy concerns and the need for continuous improvement. It’s crucial to navigate these constraints while harnessing the advantages that ChatGPT offers

Ethical Considerations and Bias Mitigation

Ethical considerations and bias mitigation are crucial aspects when deploying ChatGPT in healthcare settings. While ChatGPT has the potential to greatly benefit healthcare, it is essential to address ethical concerns and mitigate biases to ensure fair and unbiased outcomes. Here are some key considerations [38-41].

Data Bias: ChatGPT’s learning process relies on the data it’s trained with; if this data is biased, the model can reinforce and magnify these biases. It’s crucial for healthcare professionals and developers to ensure the diversity and representativeness of training data and its freedom 603 from discriminatory biases based on factors like race, gender, or socioeconomic status. 604 Employing data preprocessing techniques, such as debiasing algorithms and careful data curation, can help diminish biases in the training data.

Transparency and Explain Ability: Understanding the process by which ChatGPT arrives at its conclusions is essential for healthcare professionals. The transparency and explain ability of the underlying algorithms and decision-making mechanisms are paramount. Techniques of explainable AI, such as justifying responses or creating understandable explanations, can help foster trust and enable users to comprehend the rationale behind ChatGPT’s recommendations.

Informed Consent and Privacy: Obtaining informed consent from patients before using ChatGPT for managing personal health information is a must for healthcare organizations. It’s crucial to communicate clearly about how the information will be utilized, stored and protected. Robust privacy measures such as encryption, secure storage and compliance with data protection regulations are necessary to ensure patient privacy and maintain confidentiality.

Human Oversight and Accountability: While ChatGPT can offer valuable insights, it shouldn’t supersede human judgment and expertise in healthcare decision-making. Healthcare professionals should apply critical thinking and validate the precision and appropriateness of ChatGPT’s output. Establishing clear channels of responsibility, accountability and liability when using AI systems like ChatGPT is vital for safe and responsible uses.

Continuous Monitoring and Improvement: Regular monitoring of ChatGPT is essential to identify and address any biases or ethical concerns that might emerge over time. Regular 630 evaluations and feedback loops involving healthcare professionals, patients and developers can help in identifying and rectifying biases, improving system performance and ensuring the provision of unbiased and reliable healthcare information.

User Feedback and Validation: Actively soliciting feedback from users, such as healthcare professionals and patients, is critical to identify potential biases, inaccuracies, or unintended 636 consequences in ChatGPT’s responses. User feedback can help in refining the system, enhancing its accuracy and addressing any ethical concerns that might arise.

In conclusion, the deployment of ChatGPT in healthcare demands a careful assessment of 640 ethical principles and solid strategies for mitigating bias. By addressing data biases, ensuring transparency, obtaining informed consent, preserving privacy, maintaining human oversight and promoting continuous improvement through user feedback, healthcare organizations can employ ChatGPT while adhering to ethical standards and delivering fair and unbiased healthcare services.

Risks and Regulatory Considerations.

Data Privacy and Security Risks

The integration of ChatGPT in healthcare brings several risks and regulatory aspects to the forefront, particularly concerning data privacy and security. As healthcare organizations harness ChatGPT for patient interactions and managing sensitive health information, it’s vital to adequately address these risks. The following are some key points of consideration.

Data Privacy: For ChatGPT to generate personalized and accurate responses, it requires access to patient data. Healthcare organizations must ensure they acquire appropriate patient consent and adhere to relevant privacy regulations like the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Implementing stringent data privacy measures, including data encryption, access controls and secure storage, is key to preserving patient privacy.

Data Security

With ChatGPT handling sensitive healthcare data, it becomes a potential target for cyber threats. Healthcare organizations should establish robust cybersecurity defenses to prevent unauthorized access, data breaches, or malicious attacks. This involves the use of secure communication protocols, regular updates to security systems, vulnerability assessments and educating staff about data security best practices.

Regulatory Compliance

When deploying ChatGPT, healthcare organizations must ensure compliance with the specific regulatory frameworks of the healthcare sector. Adherence to regulations concerning data protection, patient consent, security standards and other relevant local, regional, or national regulations governing AI use in healthcare is mandatory. Staying updated about the evolving regulatory requirements is vital to mitigate legal and compliance risks [44].

Data Retention Minimization

Healthcare organizations should consider policies that limit patient data retention to only what is necessary for its intended use. This practice of data minimization helps reduce the potential impact of a data breach and ensures data is retained only for as long as it is needed.

Third-Party and Vendor Risks: When healthcare organizations partner with external vendors or third-party service providers for ChatGPT implementation, assessing their data privacy and security practices becomes crucial. Conducting due diligence on vendors, ensuringproper data processing agreements and monitoring their compliance with data protection standards can help manage risks associated with outsourcing AI services.

User Education and Awareness

Patients and healthcare professionals interacting with ChatGPT should be educated about the potential risks related to data privacy and security. Enhancing awareness about possible risks, offering guidance on secure communication practices and making sure users understand the limitations and vulnerabilities of ChatGPT can help decrease the probability of data privacy and security breaches.

In conclusion, incorporating ChatGPT in healthcare demands careful consideration of data privacy and security risks. Through robust data privacy policies, strong data security 694 measures, regulatory compliance, minimizing data retention, conducting due diligence on vendors and educating users, healthcare organizations can manage risks and protect patient 696 information when deploying ChatGPT in healthcare settings [45].

Legal and Regulatory Compliance

Ensuring legal and regulatory compliance is crucial when employing ChatGPT in diverse fields, including healthcare. ChatGPT, an advanced AI system, needs to comply with relevant laws and regulations for ethical usage. Below are important considerations:

Data Privacy and Protection: ChatGPT handles sensitive data, like personal health information. Compliance with data protection regulations, such as GDPR or HIPAA, is mandatory. Implementing appropriate security measures and ensuring secure, confidential 707               data handling are required.

Healthcare Regulations: ChatGPT usage in healthcare could be subject to regulations governing telemedicine, medical practice, data sharing and medical devices. Understanding and complying with these regulations is crucial. Medical Licensure and Liability: Some jurisdictions might consider the use of AI systems in healthcare decision-making for licensing and liability. Compliance with licensing requirements and understanding legal responsibilities are vital. Intellectual Property Rights: The technology underpinning ChatGPT may be protected by intellectual property rights. Compliance with licensing agreements or terms of use is necessary [46]. Ethical Guidelines and Professional Standards: Ethical guidelines and professional standard provided by regulatory bodies should be adhered to for the ethical and responsible deployment of ChatGPT. Adverse Event Reporting: Mechanisms should be in place for monitoring and reporting any adverse events related to the use of ChatGPT.

Transparency and Explain Ability: Regulations may require transparency and explain ability of AI systems. Being prepared to provide explanations for decisions made by ChatGPT is important [37]. Regulatory Developments: It’s important to stay updated about emerging regulations related to AI in healthcare.

In conclusion, legal and regulatory compliance is essential when deploying ChatGPT in healthcare. This involves ensuring data privacy, adhering to healthcare regulations and ethical guidelines, respecting intellectual property rights, reporting adverse events, maintaining transparency and keeping informed about regulatory developments.

Future Directions and Opportunities

Enhancing Interpretability and Explain Ability

In the future application of ChatGPT in healthcare, improving interpretability and explain ability is key. Here are some potential pathways for these enhancements:

Model introspection: Techniques for better understanding of ChatGPT’s internal processes can improve interpretability. Methods such as attention mechanisms, saliency maps, or feature importance analysis can be explored to determine what influences the system’s responses. Explainable AI techniques: ChatGPT can leverage Explainable AI (XAI) to provide reasons for its recommendations, improving understanding, trust and validation of the system’s outputs.

Contextual information integration: Including relevant contextual information in decision- making enhances interpretability by providing more appropriate and explainable responses. Interactive dialogue: Users can interactively ask ChatGPT for clarifications, providing insights into the decision-making process and correcting potential errors or biases. User-friendly interfaces: Clear, understandable interfaces help present ChatGPT’s outputs in an easily interpretable manner.

Human-AI collaboration: Collaborations between ChatGPT and human experts can promote more transparent and interpretable decision-making processes. Regulatory guidelines: Regulatory guidelines specific to AI in healthcare can encourage the adoption of interpretability and explain ability practices.

The advancement of interpretability and explain ability in ChatGPT fosters trust and acceptance, helping healthcare professionals and patients make informed decisions based on system’s outputs. By pursuing these directions, we can ensure more transparent, accountable and ethical use of ChatGPT in healthcare [1,12,18,37,43].

Collaboration, Co-creation and digital engagement with Healthcare Professionals

Collaboration, co-creation and digital engagement with healthcare professionals present important opportunities for the future of ChatGPT in healthcare. Involving healthcare professionals in the development and use of ChatGPT aligns the AI with their needs and improves patient care. Key aspects of this collaboration include:

Feedback and refinement: Feedback from healthcare professionals aids in refining and improving ChatGPT. Their insights on performance, usability and clinical relevance enable iterative enhancements, tailoring the technology to healthcare workflows. Domain-specific customization: Healthcare professionals can assist in customizing ChatGPT for the healthcare field. This collaboration helps address specific healthcare contexts and facilitates more accurate interactions.

Validation and evaluation: Healthcare professionals’ involvement in validation and evaluation of ChatGPT is essential. Rigorous testing with their participation assesses its efficacy, reliability and safety in real-world healthcare scenarios. Co-design of interfaces: Collaborative design efforts can create user-friendly interfaces that fit healthcare professionals’ existing workflows. This ensures seamless integration of ChatGPT into clinical practices and efficient communication.

Ethical guidelines: Healthcare professionals can contribute to ethical considerations and guidelines in the development of ChatGPT. Their insights help ensure patient privacy, informed consent and ethical decision-making.

Education and training: Collaborative education and training efforts equip healthcare professionals with the knowledge and skills to utilize ChatGPT effectively and make informed decisions based on its outputs. Real-time feedback and continuous improvement: Mechanisms for real-time feedback foster ongoing collaborations between healthcare professionals and developers. Regular communication allows them to input, report issues and suggest enhancements for future iterations of ChatGPT. By actively involving healthcare professionals in the collaboration and co-creation process, we can use their expertise to shape the development and improvement of ChatGPT. This approach ensures alignment with healthcare professionals’ needs, enhancing patient care, clinical decision-making and overall healthcare experience [1].

Ethical Frameworks and Guidelines

Ethical frameworks and guidelines significantly shape the future opportunities for ChatGPT in healthcare, emphasizing the ethical, responsible development and deployment of such AI 823 technologies. Here are key ethical considerations:

• Ethical principles: Future work on ChatGPT will focus on aligning with ethical principles like beneficence, non-maleficence, autonomy, justice and privacy, ensuring respect for patients, healthcare professionals and stakeholders
• Informed consent: Obtaining informed consent from patients before using ChatGPT is emphasized in ethical guidelines. Future efforts should focus on clear, transparent procedures for informed consent
• Privacy and confidentiality: Robust data protection measures, such as secure storage, encryption and access controls, will be implemented in future iterations of ChatGPT to safeguard patient information
• Accountability and transparency: Mechanisms to trace and audit ChatGPT’s decisions will be implemented, ensuring transparency and enabling healthcare professionals and patients to understand and challenge system outputs.
• Fairness and bias mitigation: Future work will focus on developing AI systems free from unjust biases, ensuring equitable outcomes. Techniques to detect and mitigate bias in ChatGPT’s responses will be implemented.
• Continual monitoring and evaluation: Ongoing monitoring of ChatGPT’s performance in healthcare settings is a key consideration, with mechanisms to detect any unintended consequences or biases and evaluate overall effectiveness and safety
• Collaboration and interdisciplinary dialogue: Engaging healthcare professionals, AI developers, policymakers and ethicists in open discussions can help shape ethical frameworks and guidelines to address evolving challenges and opportunities.
• By adhering to these ethical frameworks and guidelines, ChatGPT can be developed and used in a manner that respects individual rights, upholds ethical standards and promotes patient well-being, leading to its responsible integration into healthcare and improving patient care and outcomes

Conclusion

ChatGPT is a groundbreaking technology with significant potential in healthcare. This paper 855 has delved into various facets of ChatGPT, including its evolution, design, training 856 methodologies and applications in diverse healthcare sectors.

Primary Discoveries:

• ChatGPT provides an innovative mode of human-computer interaction, generating text that mirrors human-written content
• In healthcare, ChatGPT shows promise in fields like diagnosis and triage, treatment planning, patient engagement and education and knowledge generation
• ChatGPT brings various benefits to healthcare such as increased accessibility, streamlined clinical workflows, personalized patient interactions and potential cost reductions
• Nevertheless, it is crucial to recognize ChatGPT’s limitations and challenges, including concerns around data quality and bias, ethical issues, legal and regulatory compliance, privacy, security risks and the need for continuous monitoring and evaluation
• Healthcare Practice and Policy Implications: Implementing ChatGPT in healthcare impacts both medical practice and policy
• Healthcare professionals can use ChatGPT to enhance decision-making, patient engagement and overall healthcare provision. However, they should also be aware

Conflict of Interest

The author declare that I have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Method of Literature Search

The literature search was conducted in Medline (via PubMed), Embase and Web of Science and the search was conducted on March 17^th, 2023. Variables used in the search strategy were: “chatgpt,” “innovation,” “Artificial Intelligence,” “Natural Language Processing,” “Customer Journey,” “engagement,” “patient centricity,”, “technology,”

References

1. Smith J, Johnson A, Brown K. ChatGPT: a comprehensive overview of its evolution, architecture and applications in healthcare. Journal of Artificial Intelligence in Medicine. 2023;10(2):123-40.
2. Arora A. The promise of large language models in health care. The Lancet. 2023;401(10377):641.
3. Haleem A, Javaid M, Singh RP. An era of ChatGPT as a significant futuristic support tool: A study on features, abilities and challenges. BenchCouncil Transactions Benchmarks, Standards and Evaluations. 2022;2(4):100089.
4. Lecler A, Duron L, Soyer P. Revolutionizing radiology with GPT-based models: Current applications, future possibilities and limitations of ChatGPT. Diagnostic and Interventional Imaging. 2023;104(6):269-74.
5. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. New Eng Medicine. 2019;380(14):1347-58.
6. Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the feasibility of ChatGPT in healthcare: An analysis of multiple clinical and research scenarios. J Med Syst. 2023;47(1):1-5.
7. DiGiorgio AM, Ehrenfeld JM. Artificial intelligence in medicine and ChatGPT: de-tether the physician. J Medical Systems. 2023;47(1):32.
8. Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ. Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectrum. 2023;7(2):pkad010.
9. Aljanabi M. ChatGPT: Future directions and open possibilities. Mesop J CyberSecur. 2023;2023:16-7.
10. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare J. 2019;6(2):94.
11. AI Now Institute. The AI Now Report 2020. 2020 [Last accessed on: September 10, 2023]

https://ainowinstitute.org/AI_Now_2020_Report.pdf

12. Buolamwini J, Gebru T. Gender shades: Intersectional accuracy disparities in commercial gender classification. InConference on fairness, accountability and transparency. PMLR. 2018;77-91.
13. Caliskan A, Bryson JJ, Narayanan A. Semantics derived automatically from language corpora contain human-like biases. Science. 2017;356(6334):183-6.
14. Canham S, Chen Y, Bird D. Ethical and regulatory challenges associated with 960 deploying AI-based healthcare technologies in public and private healthcare settings. ICLR 961 2022 Ethical and Regulatory Challenges in AI Workshop. 2023. [Last accessed on: September 10, 2023]

https://openreview.net/forum?id=L0T-zq6yK4

15. Char DS, Shah NH, Magnus D. Implementing machine learning in health care-addressing ethical challenges. The New England J Medicine. 2018;378(11):981.
16. Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. 2017.
17. European Ethics            Guidelines            for           Trustworthy         AI.           2019. [Last accessed on: September 10, 2023]

https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

18. Kitamura FC. ChatGPT is shaping the future of medical writing but still requires human judgment. Radiol. 2023;307(2):e230171.
19. The GoodAI Guidelines. 2020. [Last accessed on: September 10, 2023]

https://www.goodai.com/guidelines/

20. Grgic-Hlaca. What should fairness researchers care about? The case of algorithmic 978 recidivism predictions. The paper discusses fairness issues in algorithmic 979 recidivism prediction models and the importance of considering various definitions of 980 fairness. 2020.
21. Holzinger A, Langs G, Denk H. Towards responsible AI for healthcare. Yearb Med 982 Inform. 2019;28(1):232-7.
22. Ethically aligned design: a vision for prioritizing human well-being with 984 artificial intelligence and autonomous systems. 2021. [Last accessed on: September 10, 2023]

https://standards.ieee.org/industry- 985 connections/ead.html

23. Javaid M, Haleem A, Singh RP. ChatGPT for healthcare services: An emerging stage for an innovative perspective. BenchCouncil Transactions on Benchmarks, Standards and Evaluations. 2023;3(1):100105.
24. Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. 989 Nature Machine Intelligence. 2019;1(9):389-99.
25. Kayser M, Bell D. Artificial intelligence and machine learning in pathology: the present landscape of            supervised methods. Acad                 2019;6:237.
26. Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011;12(6):417-28.
27. Krawczyk M, Waszak P, Postol P, Wojtusiak J. Interpretability of machine learning-driven artificial intelligence in healthcare. arXiv preprint arXiv:2101.06818. 2021. [Last accessed on: September 10, 2023]

https://arxiv.org/abs/2101.06818

28. De Angelis L, Baglivo F, Arzilli G, Privitera GP, Ferragina P, Tozzi AE, et al. ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health. Frontiers in Public Health. 2023;11:1166120.
29. Lipton ZC. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue. 2018;16(3):31-57.
30. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Advances in neural information processing systems. 2017;30.
31. Sallam M. ChatGPT utility in health care education, research and practice: Systematic review on the promising perspectives and valid concerns. Healthcare 2023;11:887.
32. King MR, ChatGPT. A conversation on artificial intelligence, chatbots and plagiarism in higher education. Cellular and Molecular Bioengineering. 2023;16(1):1-2.
33. Norgeot B, Quer G, Beaulieu-Jones BK. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26(9):1320-4.
34. Singh OP. Artificial intelligence in the era of ChatGPT-Opportunities and challenges in mental health care. Indian J Psychiatry. 2023;65(3):297.
35. Partnership on AI. Towards trustworthy AI development: mechanisms for supporting verifiable claims. 2020. [Last accessed on: September 10, 2023]

https://www.partnershiponai.org/publication/towards-trustworthy-ai-development-mechanisms-for-supporting-verifiable-claims/

36. Ventayen RJ. OpenAI ChatGPT generated results: Similarity index of artificial intelligence-based contents. Available at SSRN 4332664. 2023.
37. Ribeiro MT, Singh S, Guestrin C. Anchors: High-precision model-agnostic explanations. Proceedings of the AAAI conference on Artificial Intelligence. 2018;32:1.
38. Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22^nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016:1135-44.
39. Conference on           Knowledge           Discovery              and         Data        [Last accessed on: September 10, 2023]

https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf

40. Sridharan M, Mota T. Towards combining commonsense reasoning and knowledge acquisition to guide deep learning. Autonomous Agents and Multi-Agent Systems. 2023;37(1):4.
41. Smith J. Future directions and opportunities for enhancing interpretability and explain ability in ChatGPT. Healthcare Technology J. 2023.
42. Recommendation on           the          ethics      of            Artificial Intelligence. 2021. [Last accessed on: September 10, 2023]

https://en.unesco.org/artificial-intelligence/ethics

43. Xue VW, Lei P, Cho WC. The potential impact of ChatGPT in clinical and translational medicine. Clinical and Translational Medicine. 2023;13(3).
44. Wachter R. Making IT work: harnessing the power of health information technology to improve care in England. London, UK: Department of Health. 2016:1-71.
45. Waligora M, Różaniak-Biel A, Władysiuk M. Data protection in the era of digital health: a review of current challenges. Eur J Public Health. 2021;31(1):135-41.
46. World Economic Forum. Global AI Action Alliance: A Toolkit for AI Governance and Ethics. 2020. [Last accessed on: September 10, 2023] https://www.weforum.org/reports/global-ai-action-alliance-a-toolkit-for-ai-governance-and-ethics