An analytic research and review of the literature on practice of artificial intelligence in healthcare

Abstract

Artificial intelligence (AI) has transformed healthcare, particularly in robot-assisted surgery, rehabilitation, medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and adherence, and administrative applications. AI enhances pre-operative planning, intraoperative guidance, and post-operative outcomes in robotic surgery. In rehabilitation, AI enables personalized programs, physical therapy using robotics, and in real time monitoring and feedback mechanisms. The integration of AI with emerging technologies like augmented reality, virtual reality, and the Internet of Things holds promise for broader healthcare applications. However, AI adoption faces technical challenges related to data quality and bias, ethical and privacy concerns, regulatory and legal considerations, and issues of cost and accessibility. Future trends include advances in AI algorithms and robotics, integration with emerging technologies, and the potential for wider applications in healthcare and rehabilitation. Addressing ethical and security considerations is crucial for the successful integration of AI in healthcare while upholding patient safety and legal standards. Overcoming regulatory, ethical, and trust-based challenges with effective governance will be critical to the full realization of AI potential in healthcare artificial intelligence (AI)-driven healthcare solutions powered by IoT can enable in real time patient monitoring, enhancing early diagnosis and chronic illness management. AI applications in AR/VR can transform medical education by allowing healthcare professionals to practice intricate procedures in a safe environment. Wearable technology with AI-driven analytics can offer personalized health insights, facilitating proactive interventions and improved patient outcomes. Adopting these innovations can foster progress, enhance patient care, and boost overall healthcare efficiency. Future studies should refine these cross-disciplinary applications, ensure their smooth incorporation into current healthcare systems, and tackle potential ethical and security issues.

Introduction

Robotic surgery refers to the use of robotic systems as tools for performing surgeries in a precise manner and with a higher level of dexterity, whereas robotic rehabilitation is the use of robotic technology to complement physical therapy [1]. One domain that is being rapidly impacted by AI in healthcare is the use of big data analytics to enhance diagnosis, treatment, and patient care. Artificial intelligence also works in conjunction with robotic surgery and rehabilitation because it personalizes and optimizes robotic performance and results. It allows in real time evaluation and prediction in addition to enhanced visualization; both greatly enhance surgical and rehabilitative treatment [2, 3]. This paper provides insights into robot-assisted surgery and rehabilitation systems using AI to capture preciseness, patients’ rate of recovery, and prospects of these technologies in the global healthcare sector [4].

The advancement of biomedical science, encompassing genomics, digital medicine, AI, and its subset, machine learning (ML), underpins the transformation of healthcare, necessitating a new labor force and standards of practice [3]. Precision medicine, regenerative medicine, healthcare delivery, biometrics, tissue engineering, and vaccines are just a few areas that stand to benefit greatly from genomics and related technologies [5, 6].

Digital health technologies (DHTs) include mobile health (mHealth), health information technology (HIT), wearable devices, telehealth, telemedicine, mobile Internet devices (MIDs), and personalized medicine [7]. Recent technology developments that impact smart health include AI, metaverse, and data sciences [2, 6]. These technologies provide enhanced prevention, early identification of life-threatening disorders, and remote management of chronic conditions outside traditional care settings, such as wirelessly observed therapy (WOT), employing an innovative approach to monitoring therapy adherence [8]. The most promising approach is to provide and supply health services universally and at any time in an era of disruptive and minimally invasive medicine. MIDs enable recipients to access essential resources including related applications and social media platforms. The use of MIDs is extensive and allows professionals to access scientific databases, such as Medscape, Web of Science, and Scopus. The social media networks, such as YouTube, Facebook, WhatsApp, Wikipedia, and many instant messaging software platforms, are accessible to both professionals and non-professionals. The digital health modalities that employ AI in healthcare are rapidly advancing in the post-COVID-19 era [6, 9, 10].

Artificial intelligence is extensively used in diverse healthcare sectors to enhance patient health outcomes and deliver services at reduced costs. This review intends to elucidate its function in healthcare, concentrating on the following crucial aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and pharmacologic discovery, (iv) patient engagement and adherence, (v) rehabilitation, and (vi) other administrative applications [8, 11, 12]. The authors also discussed certain obstacles associated with the implementation of AI in healthcare. These findings enhance the current literature by advancing the advantages of AI tools in healthcare.

Artificial intelligence is a transforming industry, such as healthcare. From diagnostics to personalized treatment, AI technologies enhance the efficiency and accuracy of medical services. AI chatbots have transformed patient care and administration by streamlining scheduling, assisting in preliminary diagnoses, and improving engagement. These systems reduce administrative burdens, allowing for a focus on critical patient care. Research has highlighted the importance of securing AI healthcare chatbots to ensure data privacy and reliability [13].

AI plays a pivotal role in drug discovery and cancer research. Machine learning models analyze datasets to identify drug candidates, optimize treatment strategies, and predict patient therapy responses. Studies have shown that AI driven approaches enhance cancer research, particularly by using peptide data to accelerate therapy development [13]. These applications expedite research and contribute to the development of targeted and effective cancer treatments. By integrating AI solutions, healthcare has evolved towards efficient, precise, and personalized interventions. The subsequent sections explore AI in medical imaging, diagnostics, and treatment planning, emphasizing its impact on patient outcomes and healthcare efficiency.

AI in robotic-assisted surgery

Evolution of robotic-assisted surgery

Robotic surgery in surgical practice began a long time ago and has changed over the years with advances in technology. First, the surgical robots have found application in trivial operations and relatively uncomplicated procedures. The initial models of the systems, including the da Vinci Surgical System launched in the 1990s, represented a drastic transition as the system offered minimally invasive surgery, where complex operations are completed with super accuracy [14, 15]. It was later followed by the complexity of rig designs and integrations of advanced robotics systems, enhanced instrumentations, and state-of-the-art imaging systems. These systems have gradually been introduced with feedback loops and self-contained processes, which add to the effectiveness of their complex operations. Contemporary robot-assisted systems provide clients with three-dimensional visualization with high resolution, no tremble, and improved dexterity. Thus, their utility is indispensable across numerous surgical fields, including urology, gynecology, and orthopedics [16,17,18]. Increasing developments in robotics and mainframes have enhanced and voiced the quality of mechanical systems, including features such as learning algorithms and in real time data integration to improve surgical accuracy and results [19] (Fig. 1).

Fig. 1

Evolution of robotic surgery

Role of artificial intelligence in preoperative planning

Preoperative planning is the most important facet that is influenced by AI, and thus fundamentally changes the process of planning and performing surgeries. This is the process of evaluating clinical data from a patient, including radiographic images and previous surgical history, to design an appropriate surgery [14]. The AI algorithms recognize big data problems that are associated with handling pre-operative scans including CT scans and MRIs to offer great detail of the surgery simulation and increased understanding by the physician (Table 1).

Table 1 Role of artificial intelligence planning and performing surgeries

Artificial intelligence in intraoperative guidance and decision-making

Artificial intelligence has introduced a large-scale improvement in intraoperative guidance and decision-making and has improved the degree of accuracy in robot-assisted surgeries. Real-time data come from imaging systems, robotic sensors, and patient monitors, and AI algorithms are also used to aid surgical decisions [20, 21]. Some of these could include intraoperative endoscopic imaging or augmented imaging systems, in which high-resolution images can be interpreted by the advanced AI system and feedback given to the surgeons [22, 23]. One of these aids enhances the accuracy of surgery through focal structure illumination, malformation detection, and simultaneous prediction of possible complications. For instance, through image recognition using AI, doctors can detect cancerous tissues and help the surgeon in the surgery by identifying areas that need to be removed [24]. In addition, AI increases the efficiency of the movements of robotic systems involved in surgeries while also improving the ability of the system to work in the operating theater environment. Numerous analyses have indicated that the integration of AI in robotic systems can decrease intraoperative mistakes as well as enhance surgical outcomes. For instance, the application of AI in surgical systems helped reduce the operation time by 15% and increased the levels of accuracy by 10% compared to normal techniques [7, 25].

Postoperative outcomes and AI integration

It will be especially important for AI to be able to monitor the patient’s recovery process and forecast the result of the surgery. Postoperative recording data were checked in the patients’ records, biophysical probes, and imaging-associated studies to measure recovery status and the presence of post-operative complications [26]. Algorithms in AI can identify post-operative care complications through analysis of the data trends in a patient, such as the rate of breathing, blood pressure, and blood test results. This predictive capability enables early interference that may help prevent such outcomes from occurring, thus lowering the number of patients with adverse outcomes [27]. For instance, deep learning applications can predict the probability of becoming infected or being readmitted based on data analysis patterns. Therefore, an analysis of statistical data would reaffirm the proposal that AI has a positive impact on post-operative results [28]. A study conducted by Lancet showed that through the use of AI based analytical models of patient status, some readmissions have been reduced by up to 20% and patient healing time has been reduced by 12% in some of the surgeries offered by healthcare facilities [7] (Fig. 2).

Fig. 2

Artificial intelligence in cancer imaging

The following case studies show how AI has influenced the field of robotics specifically robot-assisted surgery and rehabilitation [23]. There is an example of the da Vinci Surgical System together with AI algorithms for performing prostatectomy operations. A European Urology study compared the use of the AI system with that of a non-AI system and found that there were 25% fewer positive surgical margins in the AI group and 30% better functional recovery than the non-AI system [8, 21]. The second example of AI use in rehabilitation concerns stroke patients and robotic exoskeletons. A study conducted on stroke revealed that the use of exoskeletons supported by AI had a 40% enhancement of the motor function, together with a 15% decrease in the time required for recovery as compared to conventional treatment. These examples prove that the integration of AI in robotics accelerated the systematic change in robot-assisted surgery and rehabilitation processes, which directly improved the patient’s quality of life and optimized clinical work [29, 30].

Validation and implementation

Incorporating AI models into clinical practice requires a systematic method for their selection, validation, and integration. Choosing the correct AI model requires the assessment of various elements, such as the quality of the dataset, robustness of the algorithm, and efficiency of computation. In addition, validation procedures must confirm that these models are both precise and applicable to a wide range of patient groups [2, 13]. Thorough testing in clinical environments, along with monitoring their performance in real-world scenarios, is essential for effective implementation. To support the integration of AI in healthcare, machine learning pipelines should be designed to be user-friendly, allowing clinicians to integrate AI generated insights into their decision-making processes effortlessly. A prime example is the creation of intuitive machine-learning pipelines specifically designed for medical decision-support systems. The studies have shown that structured AI workflows can improve diagnostic accuracy, as demonstrated by the use of deep learning models for COVID-19 diagnosis using CT scan analysis [13]. These frameworks offer a reproducible and understandable method for embedding AI into clinical workflows, ensuring both effectiveness and clinician confidence in AI supported decision-making. Future developments in AI model implementation are likely to concentrate on enhancing model interpretability, minimizing biases, and improving regulatory compliance to encourage widespread use in clinical environments [13].

Artificial intelligence in rehabilitation

Rehabilitation is an essential part of medical practice, as it involves the process of recovering bodily and/or mental functions after disease or injury or as a result of surgery. In the past, the rehabilitation process involved standardized pro forma and observation, which are restrictive when it comes to individualization and dynamism of the procedure [9, 31]. Artificial intelligence is currently meeting these challenges by developing new and flexible rehabilitation methods. Artificial intelligence in rehabilitation has been designed to allow computerized learning of the patient’s rehabilitative needs, developing an artificial neural network model for designing paradigms of delivering rehabilitative services, in real time evaluation of the progress of the patient, and responsive feedback which may be customized to the patient’s response [2, 32].

Artificial intelligence driven personalized rehabilitation programs

Organizational customization is one of the major advantages that is associated with the use of AI technology in rehabilitation. Unlike conventional vaccination treatment programs that aim at general treatment, artificial-intelligence-assisted programs consider numerous data from patients for the development of rehabilitation programs for every specific patient. These are records of the patient’s medical history, physical status, program accomplishments, and even genetics data [9, 33,34,35] (Table 2).

Table 2 Artificial intelligence applications in different rehabilitation programs

Artificial intelligence technology can be used to identify the exercises to be performed by a patient and the intensity and duration which when employed will give the best benefits [10]. For instance, in stroke rehabilitation, AI can examine a patient’s motor movement capability, propose a plan for addressing certain disorders, and continue to amend the program with the patient’s improvements. This flexibility ensures that patients do not get too much or too little to do, which leads to further deterioration of their condition [30].

Robotics and artificial intelligence in physical therapy

Artificial intelligence robotics is quickly changing for use in physical therapy processes. In the rehabilitation process robotic exoskeletons and other AI tools are employed to help the patient in accomplishing movements they perhaps cannot make otherwise [11]. It is especially useful for patients with many complications about their mobility, for instance, those who have undergone operations like stroke or have been paralyzed from spinal cord injuries. Artificial intelligence in these systems assists in adapting the support given by the assistive robots to the individual’s needs. For example, AI in combination with a new generation of muscular-skeletal censoring can enable constant in real time monitoring of a patient’s movements, detect the levels of assistance required, and, therefore, control the level of support provided by the exoskeleton. The dynamic interaction between the patient and robotic system improves the rehabilitation process by accomplishing exercises that lead to neural plasticity and strength [27, 28].

Monitoring and feedback systems

The specific aims of assessment in rehabilitation involve monitoring the progress of the patient to the delivery of feedback in the same process. Artificial intelligence has advanced these aspects in that monitors are present today to help deliver in real time information on the patient’s performance and progress. The mobile sensors and wearable devices are very important in gathering data during rehabilitation exercises [12]. The devices keep track of various aspects, such as the number of degrees in range for a limb, active muscle tissues, pulse rates, and many others. Decision support systems in the form of AI algorithms analyze this data to determine the quality of the exercises that have been completed and then relay this information immediately to the patient as well as the therapist [18, 21]. For example, it can recognize deviations in motor control that in most cases suggest improper form or emerging tiredness. Providing feedback as soon as the child makes a mistake is vital to eliminate such problems as clients develop other ways of making the movement that are wrong and may take a long time to correct [1].

Case studies/examples

Different studies have described the possible application of AI in the rehabilitation process. For instance, in stroke physical therapy, helper robots in the form of exoskeletons controlled by AI have been used to enable patients to regain limb functionality [14]. The people who use these devices along with conventional therapy were reported to have superior and quicker rehabilitation than those who received only conventional treatment.

Another case is the application of AI in the field of VR for cognitive rehabilitation. These systems enable the patients to go through a series of exercises to help them learn and recognize certain tasks with the AI increasing the level of difficulty depending on the patients’ response. Individual play interventions have been useful in enhancing cognitive skills in clients with TBIs and Neurological disorders [18, 20].

Challenges and limitations

Technical challenges in artificial intelligence integration

The use of AI in healthcare has been effective especially in robotic surgical operations and in the rehabilitation of patients but despite its effectiveness, it has its challenges and limitations that should be overcome for proper and acceptable use [32]. Artificial intelligence adoption in healthcare faces multiple technical issues, mostly related to data. The first and foremost problem area is the nature of the medical data which is typically large, complex and highly varied. Annotation of high-quality training data is a labor-intensive and time-consuming process involving large amounts of data [15]. However, medical data are collected from multiple sources, are unorganized, and may not be consistent, which makes it almost impossible to normalize and utilize for building an AI model. Furthermore, certain AI models depend on data quality and any form of prejudice in the data results in prejudice AI (Table 3).

Table 3 Challenges matrix

Ethical and privacy concerns

The use of AI in healthcare brings about unique ethical and privacy issues. The integration of AI in decision making especially in sensitive areas such as surgery comes with questions on liability. If the AI system is incorrect, nobody knows who is accountable for the error: the developers who made the program, the healthcare givers who converse with the artificial intelligence system or the machine [16]. The second area of concern is privacy. There is an increased probability of data breach and unauthorized access control since many AI systems require the patient’s data. The purpose of collecting patient data should be famously served and stored under the utmost secrecy to enhance confidence in AI healthcare [18].

Regulatory and legal considerations

In many cases, the advancements of AI technologies in to healthcare sector have surged ahead of the development of sound substantive legal instruments. At the moment there is no definitive set of rules that defines the application of AI in the clinical environment. This has created a gap in the legislation which makes it hard to regulate AI and guarantee the efficiency and safety of these systems [17]. Other challenges include a question of patents and ownership, or in case AI causes an error, who takes responsibility? Use of AI data in decision-making. Creating effective rules that will not inhibit the progress of AI in healthcare; while at the same time protecting the patients is a challenge that needs to be addressed [22].

Cost and accessibility issues

Thus, one of the problems that affects the deployment of AI systems is the aspect of cost-related hindrances such as the cost of developing or implementing the systems and maintaining them. Some of the enhancement technologies in the healthcare domain, including robotic surgical systems and AI based rehabilitation devices, are expensive in terms of capital which presents difficult challenges of implementation and adoption for all the healthcare facilities, especially in the LRI [18]. Price-wise, this becomes an obstacle to make it worse for those institutions or patients of color, enhancing the digital divide in the sector with a focus on more affluent institutions or the populace. As it is known, annually, new versions of systems are obtainable, personnel need to be trained, and data have to be managed, which contributes to the continuous costs [14].

Future trends and developments

Advances in artificial intelligence algorithms and robotics

The future development of AI in healthcare is demonstrated through the expansion of algorithms and robots. Advanced techniques such as deep learning and neural networks have been used to improve the accuracy and flexibility of robotic technology. These advancements will in turn help to provide better in real time interpretation of data and decision making during surgeries. Superior robotic systems will entail higher sophistication in their receptors and control mechanisms to give higher precision and fewer blunders [19]. Furthermore, AI also holds great promises for achieving higher levels of precision in using predictive analytics, especially in comprehending patient’s individual needs as well as post and pre-surgical care patterns. Reinforcement learning is an innovative technology that may enhance robotic systems' availability and learning rate, which is likely to lead to radical changes in intricate surgeries and effective rehabilitation [14] (Fig. 3).

Fig. 3

Artificial intelligence market size in healthcare

Integration with emerging technologies

There is a lot of potential when AI is combined with innovative technologies, including Augmented Reality (AR), Virtual Reality (VR), and the Internet of Things (IoT) in terms of the healthcare industry. AR and VR will improve the surgical simulation since they offer a fully realized environment where training and planning cannot only be done before the surgery but also in in real time improving the skills involved in surgery [20]. All these technologies will enable direct visualization and interaction with the three-dimensional models in a surgical environment leading to better precision and less mental preoccupation. The application of IoT will improve the monitoring of patients since it will connect different sensors and wearable devices resulting in the constant collection and analysis of data. It will also ensure better connectivity that helps in making the right decisions at the right time enhancing the patient outcomes as well as operating the hospital. Combining AI with such technologies is likely to advance research and improve the level of care delivery including robotic surgery and rehabilitation [2, 8, 11].

Potential for broader applications in healthcare and rehabilitation

Artificial intelligence applications go beyond the scope of robotic surgery and rehabilitation and embrace diagnostics, individualized therapeutic approaches and patient supervision. Sophisticated machine learning models can process large amounts of data to determine disease trends or prognosis of patient outcomes and determine a specific care plan. The use of Wearable Technology and Mobile Health Apps will improve remote monitoring and engagement of patients, thereby improving general care. In the operating room and rehabilitation vision for the future of surgical AI, the primary and sophisticated robotic systems work in concert with advanced imaging, in real time data analysis, and individual models of the patient’s response [21]. This evolution will improve the exactness and outcome of a recovery process that will in turn reduce the time taken in the recovery process hence helping to improve patients' overall well-being. Technological developments powered by AI will bring closer interaction of robotic systems with health care professionals to facilitate revolutionary changes in methods of surgery and rehabilitation [30].

Results

Artificial intelligence has applications across multiple healthcare fields, enhancing diagnostics, treatment planning, and administrative workflows. A deeper comparative analysis of model performances can provide insights into their effectiveness. Different AI models exhibit varying accuracies, efficiencies, and generalizabilities depending on the use case. Artificial intelligence shows promise in predicting COVID-19 mortality rates in ICUs. Research has explored automated machine learning techniques to predict patient outcomes based on clinical data, aiding clinicians in resource allocation and treatment strategies [36]. These predictive models help healthcare professionals identify high-risk patients earlier, potentially improving survival rates through timely intervention. Future research should refine these AI models, compare their efficacy across different datasets and patient demographics, and integrate them into clinical workflows. As AI evolves, ensuring transparency, accuracy, and usability will be crucial in maximizing its impact on healthcare outcomes [2, 17].

Discussion

Machine learning is increasingly aiding in combating antimicrobial resistance (AMR), a global health crisis that threatens current treatments. Artificial intelligence driven models analyze microbial genomes, antibiotic usage, and patient data to forecast resistance trends and optimize treatment. Recent studies have reviewed the role of machine learning in predicting AMR, offering insights for improving protocols and developing new therapies [37]. In addition to its clinical use, AI in healthcare poses security and regulatory challenges. Ensuring AI system security is crucial because of sensitive healthcare data and potential adversarial attacks. Regulatory frameworks, like the European artificial intelligence Act, govern AI in healthcare, balancing innovation with ethics and safety [38]. Compliance with these regulations is essential for trust and responsible implementation of AI in clinical settings. Addressing these challenges requires robust cybersecurity, ongoing AI model validation, and collaboration among policymakers, healthcare providers, and AI developers. Overcoming these hurdles will allow AI to transform healthcare while upholding ethical and security standards [31, 38].

Ethical and security considerations

Ethical deployment of AI in healthcare requires a careful balance between innovation and patient safety. A primary concern is data privacy. AI models rely on vast amounts of patient data, making them vulnerable to breaches and unauthorized access. Implementing robust encryption, access controls, and compliance with data protection regulations such as the General Data Protection Regulation (GDPR) is critical in mitigating these risks [30, 38]. Another key consideration was the interpretability of the model. Many AI driven decision-making systems operate as black boxes, which makes it difficult for clinicians to understand how specific predictions are generated. Enhancing transparency through explainable AI techniques can improve trust and adoption among healthcare professionals, ensuring that AI complements human expertise rather than replaces it [38].

Regulatory challenges also play a crucial role in AI deployment. Laws such as the European artificial intelligence Act impose stringent requirements on high-risk AI applications, including those used in healthcare. Ensuring compliance with these evolving regulations requires continuous updates to AI models, rigorous validation protocols, and proactive engagement with regulatory bodies to align AI advancements with legal frameworks [12, 35]. Ultimately, addressing ethical and security considerations is fundamental to the successful integration of AI into healthcare. Artificial intelligence driven healthcare solutions can enhance patient outcomes while upholding ethical and legal standards by prioritizing patient data protection, improving AI interpretability, and navigating regulatory complexities [38] (Table 4).

Table 4 AI applications in healthcare, their benefits, and associated challenges

Conclusion

They said that AI is disrupting the way robotic technologies are used in surgery and rehabilitation to increase the level of accuracy, in real time decision-making capabilities, and improvement of post-operative results. AI algorithms, robots, augmented and virtual reality, IoT, and others are in the developmental stage and will surely transform healthcare practices by allowing patients to get efficient value added with improved correctness of interventions more accurately suited to patients’ needs. They have important implications for enhancing the quality of care provided to patients as well as the efficiency of their clinical processes. Yet, to harness all these advantages, AI research and technology advancement require constant funding. Healthcare organizations should adopt these innovations to improve the quality-of-service delivery and organizational performance hence putting themselves in the frontline in terms of healthcare technology.

Summary

This review explores the applications of AI in healthcare, focusing on robot-assisted surgery, rehabilitation, medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and adherence, and administrative uses. Artificial intelligence has transformed robotic surgery by enhancing pre-operative planning, intraoperative guidance, and post-operative outcomes. In rehabilitation, AI enables personalized programs, physical therapy using robotics, and in real time monitoring and feedback mechanisms. The integration of AI with emerging technologies like augmented reality, virtual reality, and the Internet of Things holds promise for broader applications in healthcare. However, the adoption of AI faces technical challenges related to data quality and bias, ethical and privacy concerns, regulatory and legal considerations, and issues of cost and accessibility. Future trends include advances in AI algorithms and robotics, integration with emerging technologies, and the potential for wider applications in healthcare and rehabilitation. Addressing ethical and security considerations is crucial for the successful integration of AI in healthcare while upholding patient safety and legal standards.

Future directions

The integration of AI into healthcare is set to expand significantly, especially as it merges with new technologies like the Internet of Things (IoT), augmented reality/virtual reality (AR/VR), and wearable gadgets. AI solutions powered by IoT can enable in real time monitoring of patients, enhancing early diagnosis and the management of chronic illnesses. Applications of AI in AR/VR hold the promise of transforming medical education by allowing healthcare professionals to practice intricate procedures in a safe environment. Additionally, wearable technology with AI driven analytics can offer personalized health insights, facilitating proactive interventions and better patient outcomes. Future studies should aim to refine these cross-disciplinary applications, ensure their smooth incorporation into current healthcare systems, and tackle potential ethical and security issues. By adopting these innovations, AI can continue to foster progress, enhance patient care, and boost overall healthcare efficiency.