INTRODUCTION:

Artificial Intelligence (AI) refers to the simulation of human intelligence by machines, particularly computer systems. These systems are designed to perform tasks such as learning, reasoning, problem-solving, understanding language, and perception—tasks typically requiring human intelligence¹.

Artificial Intelligence (AI) is revolutionizing modern healthcare by enhancing clinical decision-making, improving diagnostic accuracy, and streamlining administrative processes. Defined as the simulation of human intelligence by machines, AI enables systems to learn, reason, and solve problems—functions that traditionally required human expertise. Over the decades, AI's entry into healthcare has been marked by key milestones, from early expert systems like MYCIN to advanced deep learning models in diagnostic imaging and genomics. Today, AI-powered tools support a wide range of applications including pathology, radiology, robotic surgery, personalized treatment, drug discovery, and remote monitoring, thereby improving patient outcomes and operational efficiency.

AI also plays a critical role in research, aiding both qualitative and quantitative methodologies and accelerating systematic reviews through automation and machine learning. Despite its transformative potential, AI in healthcare faces significant challenges. These include ethical concerns, data privacy issues, algorithmic bias, lack of human empathy, and high implementation costs. Moreover, questions of accountability and equitable access remain unresolved. Hence, while AI is a powerful ally to clinicians and researchers, it must be thoughtfully integrated to ensure it enhances rather than replaces the human touch in healthcare. Responsible deployment, interdisciplinary collaboration, and robust regulatory oversight are essential for maximizing its benefits and minimizing potential risk

Key Milestones in Ai’s Entry into Healthcare:

The entry of Artificial Intelligence (AI) into healthcare has evolved through several key milestones over the decades. The concept of AI was first introduced in the 1950s, with the term "Artificial Intelligence" coined by John McCarthy in 1956. During the 1950s and 1960s, scientists began exploring the potential of AI in various fields, including healthcare; however, limited computational power restricted its early progress.

Significant developments occurred during the 1970s and 1980s with the advent of expert systems—computer programs designed to simulate the decision-making ability of a human expert. One of the earliest AI-based healthcare applications was MYCIN, developed at Stanford University in the 1970s, which focused on diagnosing bacterial infections and recommending appropriate antibiotic treatments. Around the same time, Internist-I, created at the University of Pittsburgh in 1974, aimed to support diagnoses in internal medicine.²

The 1990s and 2000s witnessed the expansion of AI through machine learning, enabling systems to learn from data rather than relying solely on pre-defined rules. This led to innovations in predictive analytics, medical imaging, and surgical robotics. A landmark moment came in 2011 when IBM’s Watson demonstrated AI’s potential by analyzing vast amounts of medical literature to assist oncologists in selecting cancer treatments.

From the 2010s onward, modern AI has been driven by deep learning and big data analytics. These technologies have transformed healthcare by enabling early and accurate detection of diseases, supporting personalized treatment plans, and assisting in complex surgical procedures. Today, AI-powered tools play a crucial role in diagnosing cancer, cardiovascular conditions, neurological disorders, and more, marking a significant shift toward data-driven, patient-centered care.³

Ai Used in Health Care:

1. AI-Powered Diagnostic Imaging Tools (e.g., Aidoc, Zebra Medical Vision, Arterys, Qure. AI): These tools assist radiologists by using deep learning algorithms to analyze a wide range of medical images such as X-rays, CT scans, MRI, and PET scans. They can identify subtle signs of critical conditions like intracranial hemorrhage, pulmonary embolism, cancerous lesions, and spinal fractures. Some platforms offer real-time triaging—flagging life-threatening issues first for immediate review. Integration with PACS (Picture Archiving and Communication Systems) and EHRs helps streamline radiology workflows, improve turnaround time, and reduce missed diagnoses, especially in high-volume settings.⁴

2. AI-Based Pathology Platforms (e.g., PathAI, Paige.AI, Ibex Medical Analytics): These platforms digitize and analyze histopathology slides using convolutional neural networks. They detect cellular abnormalities indicative of cancers like breast, prostate, or lung cancer with high accuracy. AI can also assist in immunohistochemistry quantification and mitotic count. Some systems are being trained to predict genetic mutations from histological patterns. This AI augmentation helps reduce diagnostic variability between pathologists and supports faster, more reproducible cancer diagnosis, especially in under-resourced regions lacking trained specialists.⁵

3. Clinical Decision Support Systems (CDSS) (e.g., IBM Watson Health, MEDITECH Expanse, Elsevier ClinicalKey): CDSS tools analyze structured and unstructured clinical data including labs, imaging, genomics, and patient history to offer diagnosis support, recommend evidence-based treatments, and flag potential drug interactions. For instance, Watson for Oncology provides ranked treatment options for cancer based on global guidelines and case data. These systems are valuable for supporting less experienced clinicians, reducing diagnostic uncertainty, and personalizing treatment plans. They are increasingly embedded within EHRs for point-of-care decision-making.⁶

4. Robotic Surgery Systems (e.g., da Vinci Surgical System, Corindus CorPath

Corindus CorPath): The da Vinci system combines AI-enhanced instrumentation with surgeon-guided robotic arms that offer greater precision and minimally invasive access. It includes features like motion scaling, tremor filtration, and 3D high-definition visualization. Some advanced systems are integrating machine learning to track surgeon performance, improve ergonomics, and provide real-time feedback. AI can also be used to predict complications or optimize intraoperative decisions. Robotic surgeries have shown better patient outcomes in terms of blood loss, scarring, and recovery time.⁷

5. AI-Based Virtual Health Assistants (e.g., Buoy Health, Ada Health, Babylon Health): These AI chatbots simulate conversation with users to assess symptoms, ask clinical questions, and offer preliminary guidance. They use probabilistic models to suggest possible conditions and direct patients to the appropriate care level—home remedies, general practitioner visits, or emergency care. They operate 24/7 and are integrated with telehealth platforms in some settings. By empowering patients with instant health information, they reduce unnecessary clinic visits and aid early detection of potentially serious issues.⁸

6. AI in Intensive Care Monitoring (e.g., CLEW, Philips eICU, DeepMind Streams): These platforms continuously ingest and process ICU data from monitors, ventilators, and EMRs. AI algorithms predict patient deterioration 6–12 hours in advance, allowing proactive intervention. For example, CLEW identifies early signs of sepsis, ARDS, or cardiac decompensation. AI tools are also used in remote ICUs (tele-ICUs), allowing centralized critical care experts to oversee patients across locations. By turning high-frequency data into actionable insights, AI enhances critical care efficiency and patient safety.⁹

7. AI-Enabled Wearable Devices (e.g., Apple Watch, Fitbit, AliveCor Kardia): Modern wearables do more than track steps—they use AI to detect atrial fibrillation, fall detection, sleep patterns, and even blood oxygen changes. Some devices, like Kardia, are FDA-cleared for detecting ECG abnormalities. These devices often sync with mobile apps and healthcare portals, allowing physicians to monitor patients remotely. Continuous health tracking aids in early detection, behavior modification, and chronic disease management, promoting preventive health and reducing hospital visits.¹⁰

8. AI-Powered Drug Discovery Platforms (e.g., BenevolentAI, Atomwise, DeepMind AlphaFold): AI platforms accelerate every stage of drug development—from target identification and compound screening to toxicity prediction and clinical trial design. Atomwise uses deep learning to predict how molecules bind to targets, reducing the need for time-consuming lab testing. DeepMind’s AlphaFold solved the protein folding problem with near-experimental accuracy, helping understand disease mechanisms and enabling structure-based drug design. These platforms drastically reduce R&D costs and timelines, bringing new therapies to market faster.¹¹

9. AI-Based Administrative Tools (e.g., Olive, Nuance Dragon Medical One, Corti AI): AI is streamlining healthcare operations by automating routine tasks like medical coding, claims processing, and documentation. Tools like Nuance Dragon use speech-to-text capabilities to transcribe clinical notes directly into EHRs. Olive acts as a digital employee, navigating multiple systems to complete tasks such as verifying insurance eligibility and scheduling. These tools reduce clinician burnout, minimize human error, and free up staff to focus on patient-centered care.²

10. AI in Population Health Management (e.g., Health Catalyst, Jvion, Epic Cognitive Computing): These platforms aggregate and analyze data from multiple sources—EHRs, public health records, social determinants—to identify at-risk populations and optimize care strategies. AI can predict patients likely to develop diabetes, skip follow-ups, or require hospitalization. This enables targeted outreach, preventive care planning, and resource allocation. Some tools even provide personalized care recommendations based on behavioral data, helping reduce health disparities and manage chronic disease burdens on a community level.¹²

11. AI-Powered Retinal Screening Tools (e.g., IDx-DR, EyeArt): These AI-driven systems are specifically designed to detect diabetic retinopathy and macular edema from retinal photographs. Approved by the FDA, tools like IDx-DR can be used in primary care settings without the presence of an ophthalmologist. They analyze high-resolution images of the retina and provide instant diagnostic feedback. By enabling early detection of eye diseases that can lead to vision loss, especially in diabetic patients, these systems play a vital role in preventive care. They enhance accessibility in rural or underserved areas where eye specialists are not readily available and allow non-specialist healthcare workers to initiate timely referrals.¹³

12. AI-Based Mental Health Platforms (e.g., Woebot, Wysa): Mental health chatbots such as Woebot and Wysa use artificial intelligence to offer support based on cognitive behavioral therapy (CBT) principles. These platforms are available 24/7 and provide mood tracking, coping techniques, mindfulness exercises, and emotional check-ins. They are designed to engage users in conversation, identify distress signals, and offer immediate support or refer to professional help if needed. While they do not replace human therapists, they bridge the gap for individuals lacking access to mental health services, offering a low-cost, scalable solution to support mental well-being.¹⁴

13. AI for Dermatology (e.g., SkinVision, Derma.AI): AI dermatology apps help users detect early signs of skin cancer or other dermatological conditions by analyzing skin lesions, rashes, or moles through smartphone cameras. These tools use deep learning algorithms trained on thousands of images to assess the risk and provide recommendations. For instance, SkinVision can alert users to the need for a dermatologist visit based on risk evaluation. Such technologies empower users with proactive health behavior and can reduce late-stage diagnosis of skin cancers, especially in populations with limited access to dermatological care.¹⁵

14. AI in Genomics (e.g., DeepVariant by Google, Tempus): AI has revolutionized genomic sequencing by dramatically accelerating the process of variant calling and mutation analysis. Tools like DeepVariant interpret complex genomic data with greater accuracy and speed than traditional bioinformatics tools. These AI systems assist in identifying mutations linked to various diseases and help design personalized treatment plans, especially in oncology and rare genetic disorders. By integrating genomic insights with clinical data, AI enables precision medicine—tailoring treatment to individual genetic profiles and improving outcomes.¹⁶

15. AI-Driven Rehabilitation Devices (e.g., Fourier Intelligence, MindMaze): Advanced rehabilitation technologies incorporate AI to personalize and adapt physical therapy regimens based on real-time feedback from the patient. Devices such as robotic exoskeletons and neuro-rehabilitation platforms use sensors and machine learning to assess motor function and adjust therapy intensity. These systems are especially useful in stroke recovery and spinal cord injury rehabilitation. They not only enhance the effectiveness of therapy but also allow remote monitoring, reducing the need for constant supervision by a therapist.¹⁷

16. AI in Anesthesiology (e.g., McSleepy, iControl-RP): AI-assisted anesthetic delivery systems like McSleepy and iControl-RP monitor vital signs and adjust anesthetic levels in real-time during surgery. These systems use predictive algorithms to maintain optimal depth of anesthesia, reduce complications, and speed up recovery time. While anesthesiologists remain in charge, AI provides decision support to enhance precision, minimize human error, and improve patient safety in the operating room.¹⁸

17. AI-Powered Voice Assistants for Clinicians (e.g., Suki, Saykara): These AI-based voice assistants streamline the clinical documentation process. Doctors can dictate patient encounters, prescriptions, and orders, which the AI system automatically converts into structured notes within the electronic health record (EHR). This reduces the time spent on administrative tasks, allowing clinicians to focus more on patient care. These assistants can also retrieve patient histories or lab results upon verbal request, enhancing workflow efficiency in busy healthcare settings.

18. AI-Powered Smart Beds (e.g., Hill-Rom’s Centrella Smart+ Bed): Smart hospital beds equipped with AI and sensor technologies monitor patient movement, weight shifts, respiratory patterns, and vital signs. These beds can alert healthcare staff to potential issues such as risk of falls, bedsores, or respiratory distress. Some even integrate with hospital information systems to provide continuous updates. By automating monitoring and responding to patient needs proactively, smart beds reduce complications and improve patient safety without the constant need for manual checks.

19. AI in Radiotherapy Planning (e.g., Varian Ethos, RaySearch): Radiotherapy involves precise targeting of tumors while sparing surrounding healthy tissues. AI platforms like Varian Ethos analyze imaging data and automate treatment planning by delineating tumors and optimizing radiation dose distribution. These tools reduce the time oncologists spend on plan creation and improve treatment accuracy. The use of AI also ensures consistency and enables adaptive radiotherapy, where treatment plans are adjusted based on daily patient anatomy.¹⁹

20. AI in Emergency Response (e.g., Corti): Corti is an AI-powered system used by emergency dispatch centers to analyze real-time audio from emergency calls. It detects signs of cardiac arrest or stroke by recognizing patterns in the caller’s voice, breathing sounds, and background noise. The system provides instant feedback to dispatchers, prompting them to initiate life-saving measures like CPR instructions. This technology enhances the speed and accuracy of emergency responses and can outperform human recognition in time-critical scenarios.²⁰

Ai Used in Medical and Health Research:

AI for Qualitative Research: AI has significantly transformed qualitative research by automating tasks that were previously time-consuming and manually intensive. Through technologies like Natural Language Processing (NLP), sentiment analysis, and machine learning, AI enhances researchers' ability to interpret text, audio, video, and image data. For example, AI can transcribe interviews, detect emotional tones in voices, identify patterns in language use, and even visualize thematic maps from large datasets, allowing for deeper insights with greater efficiency.

· NVivo is one of the leading AI-supported software tools for qualitative analysis. It aids in the analysis of diverse data types including text, audio, video, and even social media content. NVivo leverages AI to auto-code qualitative data, extract themes, and generate visual representations such as word clouds and thematic charts. Its NLP capabilities help identify hidden insights while saving time through automated transcription and text mining.²¹

· ATLAS.ti empowers qualitative and mixed-methods research by using AI-driven pattern recognition and semantic analysis. It auto-codes large volumes of data, identifies complex relationships between concepts, and supports researchers in visualizing how themes connect. The AI capabilities are particularly helpful for interpreting social science research, especially when studying behaviors and attitudes across large narrative datasets.²²

· MAXQDA is another robust AI-enabled platform designed to assist with qualitative data analysis across formats like text, audio, video, and images. Its AI functions include automatic identification of recurring linguistic structures and patterns, thematic categorization, and text mining. Researchers often use MAXQDA for analyzing social media data, where massive text input and informal language can be challenging without AI.²³

· Leximancer utilizes AI-powered algorithms for text analytics and concept mapping. It analyzes the context and frequency of terms within a text to create concept maps that illustrate the relationships between ideas. It is particularly useful for identifying dominant themes across extensive textual datasets such as interview transcripts, open-ended survey responses, or literature reviews.²⁴

· Quirkos provides an intuitive platform for qualitative research by visually representing data codes in real-time. Its AI engine auto-suggests codes, facilitates theme development, and aids in organizing transcript data for analysis. Quirkos is particularly well-suited for smaller research teams or beginner qualitative researchers who benefit from its visual, user-friendly approach to managing qualitative data.

AI for Quantitative Research:

AI plays an instrumental role in enhancing quantitative research by automating statistical analysis, generating predictive models, identifying data trends, and simplifying complex data interpretation. AI-driven tools can clean, organize, and analyze large datasets quickly, while also offering sophisticated models such as regression, classification, and neural networks. This not only improves accuracy but also allows researchers to focus more on interpretation and decision-making.

· SPSS Modeler is an AI-enhanced statistical software that simplifies predictive modeling and data mining. It assists researchers in identifying patterns and building models through regression, clustering, and decision trees. SPSS Modeler automates both descriptive and inferential statistical tasks, making it especially useful in clinical trials, patient outcome analysis, and epidemiological studies. ²⁵

· R, when paired with AI-centric libraries like Caret, TensorFlow, and Keras, becomes a powerful tool for quantitative health research. It allows researchers to build and test machine learning models, automate hypothesis testing, and analyze large-scale biomedical data. These AI integrations are particularly useful in genomic studies, health forecasting, and clinical diagnostics.²⁶

· Python, another widely used tool in medical research, offers AI-driven data analysis through packages like Pandas, Scikit-learn, and Statsmodels. Python automates data preprocessing, performs deep learning for complex tasks like medical image analysis, and helps researchers detect trends and correlations within vast health-related datasets. It's especially popular due to its flexibility and strong support for AI workflows.

· IBM Watson Analytics brings AI to the forefront of decision-making by offering automated statistical analyses and predictive modeling. It helps researchers uncover hidden patterns in clinical or population data, offers intelligent data visualizations, and supports personalized recommendations in healthcare research, especially when handling complex multidimensional data.

· JASP and Jamovi are user-friendly, open-source tools that integrate AI for statistical analysis and Bayesian modeling. They are ideal for researchers with limited coding experience who seek easy-to-use platforms for hypothesis testing, graphical output, and data visualization. Both platforms improve accessibility to advanced statistics while maintaining AI support for automation and interpretation.

AI for Systematic Reviews and Meta-Analysis: Systematic reviews and meta-analyses are critical for evidence-based practice but are traditionally labor-intensive and time-consuming. AI accelerates and refines these processes by automating literature screening, data extraction, duplication removal, risk of bias assessment, and statistical synthesis. AI tools help researchers save time while improving consistency, reliability, and reproducibility of review outcomes.

· Covidence is a widely used AI tool that automates the screening and extraction processes involved in systematic reviews. It enables rapid sorting of articles based on relevance, identifies duplicates, and facilitates data collection with minimal manual intervention. This tool is especially valuable for Cochrane-style reviews and guideline development where large literature volumes are reviewed.

· Rayyan uses AI and machine learning to assist in the abstract and title screening phase of systematic reviews. Its algorithm learns from user input and improves its screening suggestions over time, ranking studies based on likelihood of inclusion. Rayyan also enables multiple reviewers to collaborate seamlessly while detecting duplicated entries and potential biases.

· Abstrackr supports abstract screening by predicting the relevance of studies using machine learning algorithms. It automates the process of sorting through large databases by suggesting which studies are most likely to meet inclusion criteria. This tool helps reduce reviewer fatigue and enhances the efficiency of large-scale evidence synthesis.

· RobotReviewer is an AI assistant designed to evaluate risk of bias in studies automatically. It analyzes study characteristics, identifies biases in reporting and selection, and flags methodological concerns, significantly reducing the time and subjectivity involved in traditional bias assessments.

· EPPI-Reviewer integrates AI for data extraction, meta-analysis computation, and graphical visualization. It supports various study designs and offers automated coding and thematic clustering. This tool is ideal for mixed-methods systematic reviews where both qualitative and quantitative data need to be analyzed simultaneously.

· Systematic Review Assistant (SRA Helper) is a comprehensive AI platform that supports study selection, keyword clustering, and categorization. It helps streamline the entire systematic review process by minimizing manual screening effort and enabling rapid literature mapping and classification. SRA Helper enhances meta-analysis by facilitating clear grouping of studies based on AI-recognized themes or outcomes.²⁷

How Ai Replaces Or Enhances Human Decisions In Healthcare:

Artificial Intelligence (AI) is increasingly transforming healthcare by enhancing or even replacing certain aspects of human decision-making. One of its most significant contributions lies in data analysis, where AI can process vast amounts of structured and unstructured data—such as electronic medical records, lab reports, and imaging results—much faster and more accurately than humans. This capability allows for the early identification of risk factors, trends, and clinical insights, improving the speed and quality of care. In diagnostics, AI supports greater accuracy by detecting subtle patterns in radiological images, pathology slides, and laboratory values, which might otherwise be overlooked by clinicians. For instance, in oncology, cardiology, and neurology, AI aids in the early detection and classification of diseases, reducing the risk of misdiagnosis.

AI also contributes to personalized treatment planning by integrating patient-specific data—such as genetic information, lifestyle habits, and medical history—to tailor interventions that optimize health outcomes. Predictive analytics is another area where AI excels, forecasting the likelihood of complications like sepsis or hospital readmission before symptoms become clinically evident, allowing for timely preventive measures. In drug development, AI accelerates the discovery process by modeling drug interactions, predicting toxicity, and identifying promising molecules, thereby reducing the time and cost involved in bringing new medications to market.

Furthermore, AI enhances robotic-assisted surgeries by improving precision, filtering hand tremors, and assisting in surgical planning, which leads to fewer errors and quicker recovery for patients. Virtual assistants and chatbots powered by AI help triage patients, offer basic health advice, and streamline appointments, improving access to care and reducing the burden on healthcare staff. In the realm of mental health, AI can analyze speech patterns, facial expressions, and digital behavior to detect early signs of conditions such as depression and anxiety, prompting timely interventions.

AI also plays a vital role in administrative decision-making by predicting hospital admission trends, optimizing staffing, and managing inventory, ultimately improving operational efficiency. Importantly, AI can help minimize human biases in clinical judgments by offering data-driven, objective recommendations, promoting more equitable healthcare decisions. While AI cannot entirely replace the human touch in healthcare, it serves as a powerful tool to support clinicians, enhance precision, increase efficiency, and improve patient outcomes^.28,29

"Challenges and Limitations of Ai in Healthcare"

While Artificial Intelligence (AI) has significantly advanced healthcare, it also presents several disadvantages that must be carefully considered. One of the primary concerns is the lack of a human touch—AI systems cannot provide empathy, compassion, or emotional support, which are essential elements of patient-centered care, particularly in situations involving mental health, palliative care, or sensitive diagnoses. Additionally, AI relies heavily on large volumes of patient data, raising concerns about privacy and data security. Any breach or misuse can compromise confidential health information.

Another major issue is bias and discrimination. If the data used to train AI algorithms is not diverse or contains historical biases, the outcomes may be skewed, potentially disadvantaging certain patient groups. Overdependence on AI can also lead to a decline in clinical judgment and critical thinking skills among healthcare providers, especially when the technology fails or yields incorrect results. There is also a growing concern over job displacement, particularly in roles involving routine diagnostics or administrative tasks.

Accountability is another grey area—when AI makes a wrong diagnosis or treatment recommendation, it is often unclear who is responsible: the developer, the clinician, or the healthcare institution. Moreover, AI systems are expensive to develop and implement, making them inaccessible to low-resource settings and potentially widening the gap in healthcare equity. Many clinicians may also lack the technical training to effectively use or interpret AI tools, leading to errors or distrust in the technology. Finally, the pace of AI development often outstrips regulatory frameworks, posing ethical challenges and increasing the risk of patient harm due to insufficient oversight.

Despite its potential, these limitations highlight the importance of integrating AI thoughtfully and responsibly into healthcare, ensuring it complements rather than replaces the essential human aspects of medical care.^30,31

CONCLUSION:

The integration of Artificial Intelligence (AI) into healthcare has significantly transformed clinical practice, diagnostics, and patient care. AI’s ability to analyze vast datasets, detect patterns, and enhance decision-making has led to remarkable advancements in disease detection, robotic-assisted surgeries, personalized treatment, and administrative efficiency. Its applications in medical imaging, pathology, genomics, and predictive analytics have improved accuracy and early intervention, ultimately optimizing patient outcomes. However, despite its numerous benefits, AI poses challenges, including concerns about data security, algorithmic biases, job displacement, and the ethical implications of decision-making. Additionally, AI lacks the human touch required for compassionate patient care, emphasizing the need for careful implementation. While AI cannot replace healthcare professionals, it serves as a crucial tool in augmenting clinical expertise and improving efficiency. Future efforts must focus on responsible AI development, ensuring inclusivity, transparency, and collaboration between technology and human intelligence to create a more effective and ethical healthcare system.

REFERENCES:

1. Mohanasundari S K, Kalpana M, Madhusudhan U, et al. Can Artificial Intelligence Replace the Unique Nursing Role? Cureus 2023; 15(12): e51150.doi:10.7759/cureus.51150.

2. Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023; 23: 689. https://doi.org/10.1186/s12909-023-04698-z

3. Basu K, Sinha R, Ong A, Basu T. Artificial Intelligence: How is It Changing Medical Sciences and Its Future? Indian J Dermatol. 2020; 65(5): 365-370. doi: 10.4103/ijd.IJD_421_20. PMID: 33165420; PMCID: PMC7640807.

4. Zavaleta-Monestel E, Quesada-Villaseñor R, Arguedas-Chacón S, García-Montero J, Barrantes-López M, Salas-Segura J, Anchía-Alfaro A, Nieto-Bernal D, Diaz-Juan DE. Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment. Cureus. 2024 Jun 3; 16(6): e61585. doi: 10.7759/cureus.61585. PMID: 38962585; PMCID: PMC11221395.

5. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019 Nov; 16(11): 703-715. doi: 10.1038/s41571-019-0252-y. Epub 2019 Aug 9. PMID: 31399699; PMCID: PMC6880861.

6. Wasylewicz ATM, Scheepers-Hoeks AMJW. Clinical Decision Support Systems. 2018 Dec 22. In: Kubben P, Dumontier M, Dekker A, editors. Fundamentals of Clinical Data Science [Internet]. Cham (CH): Springer; 2019. Chapter 11. Available from: https://www.ncbi.nlm.nih.gov/books/NBK543516/ doi: 10.1007/978-3-319-99713-1_11

7. Rivero-Moreno Y, Echevarria S, Vidal-Valderrama C, Pianetti L, Cordova-Guilarte J, Navarro-Gonzalez J, Acevedo-Rodríguez J, Dorado-Avila G, Osorio-Romero L, Chavez-Campos C, Acero-Alvarracín K. Robotic Surgery: A Comprehensive Review of the Literature and Current Trends. Cureus. 2023 Jul 24; 15(7): e42370. doi: 10.7759/cureus.42370. PMID: 37621804; PMCID: PMC10445506.

8. Clark M, Bailey S; Authors. Chatbots in Health Care: Connecting Patients to Information: Emerging Health Technologies [Internet]. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health; 2024 Jan. Available from: https://www.ncbi.nlm.nih.gov/books/NBK602381/

9. Suresh V, Singh KK, Vaish E, Gurjar M, Ambuli Nambi A, Khulbe Y, Muzaffar S. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus. 2024 May 7; 16(5): e59797. doi: 10.7759/cureus.59797. PMID: 38846182; PMCID: PMC11154024.

10. Papalamprakopoulou Z, Stavropoulos D, Moustakidis S, Avgerinos D, Efremidis M, Kampaktsis PN. Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives. Front Cardiovasc Med. 2024 Jul 15; 11: 1432876. doi: 10.3389/fcvm.2024.1432876. PMID: 39077110; PMCID: PMC11284169.

11. Serrano DR, Luciano FC, Anaya BJ, Ongoren B, Kara A, Molina G, Ramirez BI, Sánchez-Guirales SA, Simon JA, Tomietto G, Rapti C, Ruiz HK, Rawat S, Kumar D, Lalatsa A. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics. 2024 Oct 14; 16(10): 1328. doi: 10.3390/pharmaceutics16101328. PMID: 39458657; PMCID: PMC11510778.

12. Thomas Craig KJ, Fusco N, Gunnarsdottir T, Chamberland L, Snowdon JL, Kassler WJ. Leveraging Data and Digital Health Technologies to Assess and Impact Social Determinants of Health (SDoH): a State-of-the-Art Literature Review. Online J Public Health Inform. 2021 Dec 24; 13(3): E14. doi: 10.5210/ojphi. v13i3.11081. PMID: 35082976; PMCID: PMC8765800.

13. Xu X, Zhang M, Huang S, Li X, Kui X, Liu J. The application of artificial intelligence in diabetic retinopathy: progress and prospects. Front Cell Dev Biol. 2024 Oct 25; 12:1473176. doi: 10.3389/fcell.2024.1473176. PMID: 39524224; PMCID: PMC11543434.

14. Haque MDR, Rubya S. An Overview of Chatbot-Based Mobile Mental Health Apps: Insights from App Description and User Reviews. JMIR Mhealth Uhealth. 2023 May 22; 11: e44838. doi: 10.2196/44838. PMID: 37213181; PMCID: PMC10242473.

15. Smak Gregoor AM, Sangers TE, Bakker LJ, Hollestein L, Uyl-de Groot CA, Nijsten T, Wakkee M. An artificial intelligence-based app for skin cancer detection evaluated in a population-based setting. NPJ Digit Med. 2023 May 20; 6(1): 90. doi: 10.1038/s41746-023-00831-w. PMID: 37210466; PMCID: PMC10199884.

16. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol. 2022 Jun 15; 39(8): 120. doi: 10.1007/s12032-022-01711-1. PMID: 35704152; PMCID: PMC9198206.

17. Rasa AR. Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements. Biomed Res Int. 2024 Dec 17; 2024: 9554590. doi: 10.1155/bmri/9554590. PMID: 39720127; PMCID: PMC11668540.

18. Kambale M, Jadhav S. Applications of artificial intelligence in anesthesia: A systematic review. Saudi J Anaesth. 2024 Apr-Jun; 18(2): 249-256. doi: 10.4103/sja.sja_955_23. Epub 2024 Mar 14. PMID: 38654854; PMCID: PMC11033896.

19. Siddique S, Chow JCL. Artificial intelligence in radiotherapy. Rep Pract Oncol Radiother. 2020 Jul-Aug; 25(4): 656-666. doi: 10.1016/j.rpor.2020.03.015. Epub 2020 May 6. PMID: 32617080; PMCID: PMC7321818.

20. Scholz ML, Collatz-Christensen H, Blomberg SNF, Boebel S, Verhoeven J, Krafft T. Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point. Scand J Trauma Resusc Emerg Med. 2022 May 12; 30(1): 36. doi: 10.1186/s13049-022-01020-6. PMID: 35549978; PMCID: PMC9097123.

21. Dhakal K. NVivo. J Med Libr Assoc. 2022 Apr 1;110(2):270-272. doi: 10.5195/jmla.2022.1271. PMID: 35440911; PMCID: PMC9014916.

22. Ñañez-Silva MV, Quispe-Calderón JC, Huallpa P, Larico-Quispe BN. Analysis of academic research data with the use of ATLAS.ti: Experiences of use in the area of Tourism and Hospitality Administration. Data Metadata. 2024; 3:306. doi:10.56294/dm2024306.

23. Kuckartz U, Rädiker S. Analyzing Qualitative Data with MAXQDA: Text, Audio, and Video. 2019. doi:10.1007/978-3-030-15671-8.

24. Crofts K, Bisman J. Interrogating accountability: An illustration of the use of Leximancer software for qualitative data analysis. Qual Res Account Manag. 2010; 7(2): 180-207. doi:10.1108/11766091011050859.

25. Wendler T, Gröttrup S. Data Mining with SPSS Modeler. 1st ed. Cham: Springer; 2016. DOI: 10.1007/978-3-319-28709-6. ISBN: 978-3-319-28707-2.

26. Badrulhisham F, Pogatzki-Zahn E, Segelcke D, Spisak T, Vollert J. Machine learning and artificial intelligence in neuroscience: A primer for researchers. Brain Behav Immun. 2024; 115: 470-9. doi: 10.1016/j.bbi.2023.11.005.

27. Fabiano N, Gupta A, Bhambra N, Luu B, Wong S, Maaz M, Fiedorowicz JG, Smith AL, Solmi M. How to optimize the systematic review process using AI tools. JCPP Adv. 2024 Apr 23; 4(2): e12234. doi: 10.1002/jcv2.12234. PMID: 38827982; PMCID: PMC11143948.

28. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021 Jul; 8(2): e188-e194. doi: 10.7861/fhj.2021-0095. PMID: 34286183; PMCID: PMC8285156.

29. Khosravi M, Zare Z, Mojtabaeian SM, Izadi R. Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews. Health Serv Res Manag Epidemiol. 2024 Mar 5; 11:23333928241234863. doi: 10.1177/23333928241234863. PMID: 38449840; PMCID: PMC10916499.

30. Kelly, C.J., Karthikesalingam, A., Suleyman, M. et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17, 195 (2019). https://doi.org/10.1186/s12916-019-1426-2

31. Devasis P, Atul V, Simerpreet S. A Critical Review on Challenges and Limitations in Artificial Intelligence-Based e-Health Applications. Eng Technol Open Acc. 2023; 5(1): 555654. DOI: 10.19080/ETOAJ.2023.05.555654

Received on 07.04.2025 Revised on 25.04.2025

Accepted on 10.05.2025 Published on 21.05.2025

Available online from May 22, 2025

Asian J. Nursing Education and Research. 2025;15(2):111-118.

DOI: 10.52711/2349-2996.2025.00025

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