This study adopts a comprehensive literature review technique to evaluate the influence and applications of artificial intelligence (AI) in healthcare. The technique is structured into three primary stages: data gathering, data analysis, and synthesis of key findings.

The first phase entailed identifying relevant literature on the application of AI in healthcare. A thorough search was undertaken utilizing academic databases, including PubMed, IEEE Xplore, Google Scholar, and ScienceDirect, to extract peer-reviewed articles, conference papers, reports, and case studies published between 2010 and 2023. Search phrases included “artificial intelligence in healthcare”, “AI-assisted medical diagnosis”, “robot-assisted surgery”, “AI in drug discovery”, “stroke management using AI”, and “artificial neurons in medicine”. Boolean operators like “AND” and “OR” were employed to ensure a comprehensive search. Inclusion criteria were as follows:

Exclusion criteria included studies that focused only on AI outside of healthcare, non-peer-reviewed articles, and papers not directly addressing the major areas indicated in the goal of this research.

The analysis phase includes categorizing the literature based on the major emphasis of the AI application. Articles were categorized into five categories:

1) AI-assisted medical diagnostics,

2) Drug development and medical research,

3) Robot-assisted surgery,

4) AI in stroke therapy, and

5) Artificial neurons. Key details from each publication were retrieved, including the individual AI technologies mentioned, their healthcare applications, and any highlighted ethical, technological, or social concerns.

The qualitative analysis method was utilized to discover repeating themes, trends, and issues in the literature. This strategy helps to integrate results on the practical applications of AI and the ethical challenges that follow its adoption in healthcare settings. Textual data were tagged based on important themes, and commonalities were mapped across diverse research to find patterns and emerging technologies in AI healthcare applications.

The third phase entailed combining the results from the analyzed literature. The key findings were categorized according to the different uses of AI in healthcare, with an emphasis on the technological breakthroughs, benefits, and limitations. Ethical considerations such as privacy, patient autonomy, data security, and the repercussions of AI-driven judgments in healthcare were also explored.

This assessment technique gives an organized and complete overview of how AI is currently being deployed in healthcare, showing both its transformational potential and the limitations that need to be solved for wider adoption.

Artificial Intelligence is widely applied in medicine, where it can help progress treatment, care for patients with chronic illnesses, suggest targeted actions for complex conditions, and enhance the standard of medical care. Additionally, the development of different AI techniques has led to the early detection, identification, and management of referrals for diseases [41] . AI is a potent image analysis technology that radiology experts are using more and more to reduce diagnostic errors in the context of prevention and to diagnose various diseases early.

Artificial intelligence (AI) is a clever and useful technology that cardiologists can utilize to enhance their decision-making while evaluating ECG and echocardiogram charts. According to a paper from an Oxford hospital, the Ultromics platform uses artificial intelligence (AI) to analyze echocardiogram scans, which identify ischemic heart disease by sensing heartbeat patterns [42] . Using body imaging modalities, AI has also shown promising results in the early diagnosis of diseases like skin and breast cancer, eye conditions, and pneumonia [43] . The onset of diabetes was predicted in real time using ML models in a recent study. Based on the findings, the most effective model for predicting the various diabetes factors was a two-class augmented decision tree [44] .

An AI-powered computed tomography device aid is the AI-RAD Companion Chest CT. The AI-RAD companion automates picture dataset post-processing using AI-powered algorithms. Automating routine procedures with repetitive steps and a large patient load can let radiologists concentrate on more important matters. Compared to hand comments, which take 30 minutes to calculate severity scores, our system can do it in roughly 10 seconds every case. These results can be used to monitor the progression of abnormalities in COVID-19 patients and promptly assess the severity of the lung infection [45] . From the day of its founding to the present, these developments show how artificial intelligence (AI) has surpassed human intelligence. In some tasks, it’s predicted that AI will eventually outperform humans, which could be advantageous for people [46] . By using AI-powered algorithms to generate medical images, AIRad Companion helps radiologists diagnose patients more accurately and with less effort and error.

As can be seen above, In the realm of artificial intelligence (AI) applications in medical imaging, both the Ultromics platform and the AI-RAD Companion Chest CT stand out as powerful tools. However, they cater to different medical needs and exhibit unique trade-offs in functionality, precision, and scope. The Ultromics platform and AI-RAD Companion Chest CT represent two transformative AI-driven tools in medical imaging, each catering to distinct needs. Ultromics specializes in echocardiography, offering unmatched precision in cardiac imaging, predictive analytics for heart disease progression, and automation of manual measurements, making it ideal for cardiology-focused institutions. However, its limited scope and high cost may restrict adoption. In contrast, AI-RAD Companion Chest CT supports chest CT imaging, offering broader functionality by analyzing lung, cardiovascular, and musculoskeletal conditions. It seamlessly inte-grates into existing workflows, automates quantifications, and provides cost-effective multi-organ diagnostics, making it suitable for multidisciplinary healthcare facilities. While Ultromics offers superior accuracy in its niche, AI-RAD Companion delivers versatility and value for diverse chest imaging needs. The trade-off between specialization and generalization, cost, and integration capabilities makes the choice highly dependent on an institution's clinical focus and resource availability. Both tools demonstrate the potential of AI in transforming healthcare.

Drug discovery AI methods have evolved from machine learning, bioinformatics, and cheminformatics models [47] . These innovations can significantly cut down on the high expense and length of time associated with finding new drugs [48] . Pharmaceutical science has been paying close attention to artificial intelligence technologies as deep learning algorithms have produced better outcomes when predicting qualities. AI’s use in early medication development has significantly increased. According to a previous study, Eve, an AI-based robot scientist, completed the medication development process quickly and affordably [49] . Moreover, the development of AI tools in healthcare fosters the creation of novel COVID-19 viral genotypes and variations. Also, it helps in the development of medications and vaccines to obtain potent therapeutic and preventive agents for containing the COVID-19 pandemic [50] .

Researchers at Hyderabad, India’s TCS Innovation Labs are using artificial intelligence (AI) to find new chemicals that could target particular regions of the novel coronavirus (SARS-CoV-2) [51] . Artificial Intelligence in Medicine (AIM) has seen a substantial growth in specialization in recent years due to the growing impact of AI on the medical field. This underscores the necessity of conducting a systematic review of study findings and advancements in Artificial Intelligence Medicine. Microsoft’s Hanover Project in Oregon has looked at scientific data to personalize a patient’s cancer treatment plan. Generative AI can be utilized in clinical research to generate synthetic data that will improve datasets and boost diversity [52] .

Furthermore, metaverse apps allow researchers to conduct experiments in a controlled and immersive environment. Using the metaverse can help researchers who are physically separated from one another in their study collaboration. Through the metaverse, they can do joint study in a virtual environment that is comparable to that of researchers in the same room [53] . ChatGPT, another AI-based application, may be utilized in clinical studies to facilitate data gathering and offer details regarding clinical trials. It can assist in identifying important findings and distilling relevant papers, enabling medical researchers to skillfully manage vast amounts of Internet-based material. Furthermore, ChatGPT is utilized by a chatbot to assist medical researchers in interpreting medical terminology. But if chatbots are used in medical research, further moral questions might surface [54] .

Over 4000 surgical robots are utilized in practice every day, demonstrating the widespread adoption of these devices [55] . Robotic surgical instruments enable physicians to make incisions in a patient’s body and carry out various surgical procedures. This kind of surgery has the potential to lessen pain, bleeding, scarring, infection, and recovery time following surgery as compared to normal surgical methods. Controlled access surgery has been transformed by the development of robotic technology, which have addressed some of the drawbacks of the laparoscopic approach [56] . Robotic systems are not meant to replace surgeons or do tasks on their own; rather, they are designed to function more ergonomically and with greater flexibility. That is why they are called “master-slave systems”-the surgeon operates them [57] .

One common and frequently occurring condition that claims lives is stroke [58] . Recent years have seen an increase in the use of AI techniques in stroke-related trials, particularly in the three critical areas of stroke care-early illness prediction, diagnosis, and treatment [59] . Machine learning programs may be able to distinguish an ischemic stroke from a hemorrhagic or any other type of stroke, reducing the risk of ignoring cases such as meningitis, coma, encephalitis, acute demyelination, abscess, and subdural hematoma.

Alzheimer’s disease is a neurological disorder that causes emotional, behavioral, and motor problems as well as the gradual loss of a person’s spirit. It is devastating for both the patient and their family [60] . Artificial neurons that might be implanted into the brain to heal damage caused by neurodegenerative disorders like Alzheimer’s disease have been developed by an international research team. The researchers created tiny silicon devices called chips that mimic the operation of real neurons by simulating biological ion channels [61] . The goal is to create these chips in order to fix the essential functions of the nervous system and undo the damage caused by autoimmune reactions. The silicon chips are perfect for use as medical implants or in other bioelectronic devices because they function similarly to biological neurons and only require 140 nanowatts of electricity, which is one-billionth of the power required by the microprocessors used to create artificial neurons [36] .

In theory, AI models should have straightforward characteristics and capabilities so that Health Care Professionals (HCPs) can effectively use them [62] . However, there are a few obstacles to the use of AI in healthcare. These include the high cost of enhancing data validity, the difficulty of creating and maintaining the IT infrastructure needed to support the AI process, and the increased expenses of storing and backing up data for research. Furthermore, bias, brittleness, and inapplicability outside of the training domain are only a few of the flaws that AI algorithms may have [63] . In order to solve the concerns with cost, technology infrastructure, and the use of AI systems for HCPs, healthcare providers should create and execute an efficient strategic plan for using AI in healthcare. According to [64] , physicians’ opinions on the use of AI in healthcare were influenced by a number of factors, including workload, desire to acquire AI training, risks associated with using AI, and its reliability. Another factor that has been shown to impede the use of AI is the absence of accountability for AI. To enable AI to be used securely in the future, it is advised that medical and nursing curriculum include AI training.

The effectiveness of AI raises ethical concerns about a number of issues, including accountability when AI is used in decision-making, the capacity of AI to make incorrect judgments, issues with AI yield authentication, the confirmation of the protection of sensitive data, inherent biases in the data used in AI system tests, preserving public confidence in the development and benefits of AI systems, impacting the public’s sense of dignity and social isolation in care settings, implications for HCPs’ roles and skill requirements, and the potential for AI to be used maliciously.

AI may provide safety and reliability challenges when it comes to decision-making, treatment delivery, and equipment control in the healthcare industry. AI may make mistakes that are difficult to identify or have unfavorable outcomes that could have dire repercussions [65] . Furthermore, explainability is a primary barrier to AI’s application in a variety of fields. In order to increase the acceptance of AI-based applications in the decision-critical sector and overcome the lack of understanding of these applications, “Explainable artificial intelligence (XAI),” a branch of AI research, is necessary [66] . XAI in healthcare enables physicians and patients to understand the reasoning behind diagnosis conclusions made by AI [67] . Because these healthcare applications use private, sensitive data that is restricted by legal restrictions, they also present data privacy and security challenges. If sensitive medical data needs to be analyzed without decrypting it, homomorphic encryption allows computations to be performed on encrypted data. Though resource-intensive, this can be useful for scenarios like research where direct access to plaintext data is not necessary [68] [69] . AI may be used to protect healthcare computers against cyberattacks and identify them, but it can also be exploited to access sensitive data or flooded with inaccurate or biased information in ways that make it difficult to track down [70] . Therefore, before incorporating AI into healthcare systems, it is important to emphasize key medical-ethical values including beneficence, autonomy, equity, and non-maleficence [71] .

Ethical considerations in AI-powered healthcare are exemplified by situations concerning bias, transparency, and accountability. For example, a 2019 study demonstrated that an AI system employed by U.S. healthcare providers to allocate resources significantly preferred white patients over Black patients, even when both groups exhibited equal medical needs [72] . This occurred because the algorithm relied on healthcare spending as a proxy for health state, inadvertently reflecting existing racial inequities in access to care. This case emphasizes the need of using representative datasets to train AI algorithms to ensure equitable treatment.

Another instance is AI applications in cancer diagnoses, where technologies like IBM Watson for Oncology attracted criticism for making therapy recommendations that were not necessarily clinically proven [73] . The lack of explainability in how these AI systems obtained their results produced trust concerns among clinicians, who were unwilling to adopt them in practice. This underlines the need for transparency and explainable AI (XAI) to promote reliability in clinical decision-making.