European Journal of Cardiovascular Medicine

Artificial intelligence (AI) is transforming medicine by improving the accuracy and efficiency of clinical decision-making, lowering diagnostic errors, and enabling tailored treatment plans. AI-powered technologies, such as machine learning, deep learning, and natural language processing, enable healthcare practitioners to evaluate massive volumes of patient data, find complicated patterns, and make evidence-based predictions. Radiology, pathology, cardiology, and cancer are among the specialties that benefit the most from these capabilities [1-3].

In radiology, AI-powered image recognition algorithms help discover abnormalities in medical scans, allowing for early diagnosis of illnesses such as cancer, fractures, and neurological problems [4, 5]. Similarly, in pathology, AI aids in the accurate analysis of biopsy slides, hence enhancing the diagnosis of cancers and other disorders [6]. In cardiology, machine learning algorithms help to assess heart disease risk, anticipate cardiac events, and optimize treatment approaches for specific individuals [7]. AI also plays an important role in oncology by recognizing tumour features, driving individualized chemotherapy regimens, and predicting patient reactions to certain treatments [8].

Beyond diagnoses and treatment planning, artificial intelligence is changing healthcare operations such as electronic health record administration, virtual patient consultations, and robotic-assisted surgeries [9]. AI-powered chatbots and virtual assistants offer 24-hour patient support, addressing medical questions and assisting with medication adherence [10]. In robotic surgery, AI improves precision while decreasing the likelihood of complications, resulting in speedier recovery periods and better patient outcomes [11].

The integration of AI in medicine is not without hurdles, including issues about data privacy, bias in AI algorithms, and the interpretability of AI-driven conclusions. However, with continued technological breakthroughs and more collaboration between medical experts and AI researchers, AI is projected to reshape healthcare, resulting in better diagnosis, more effective treatment options, and overall improved patient care [12].

2. AI for Medical Diagnosis

Artificial intelligence has considerably increased diagnostic accuracy by evaluating complex medical data with high precision. AI can process massive volumes of clinical data quickly, find subtle trends, and improve disease identification across a wide range of medical specializations by leveraging machine learning and deep learning algorithms [13]. Table 1 emphasizes the transformation from traditional to AI-enhanced diagnostic methods, showing the practical improvements in various aspects of the diagnostic process.

2.1 AI in Radiology

AI-powered imaging tools help to detect anomalies in X-rays, CT scans, MRIs, and ultrasounds [14]. Convolutional neural networks (CNNs) are essential for assessing medical images and detecting illnesses such as lung nodules, brain malignancies, and fractures [15]. These AI technologies not only lower radiologists' burden, but they also increase diagnostic accuracy by reducing human error while enhancing efficiency.

2.2 AI in Pathology

AI-driven histopathological analysis has helped digital pathology by allowing for the precise detection of malignant and pre-cancerous alterations in tissue samples [16]. AI models can differentiate between benign and cancerous cells, allowing pathologists to make faster and more accurate diagnoses. AI also helps medics forecast illness progression, allowing them to personalize treatments based on individual patient data [17].

2.3 AI in dermatology

AI-powered diagnostic tools examine skin lesions, rashes, and moles to identify diseases such as melanoma, psoriasis, and eczema [18]. Machine learning algorithms trained on large dermatology datasets can grade skin photos with similar accuracy to dermatologists. Furthermore, AI-powered smartphone apps enable users to take photographs of their skin for preliminary evaluations, facilitating early identification and eliminating unnecessary clinical visits [19].

2.4 AI in ophthalmology

AI has dramatically improved the early detection of diabetic retinopathy, glaucoma, and macular degeneration by analyzing retinal images [20]. Deep learning algorithms discover patterns linked with eye disorders, allowing for early diagnosis and therapy, hence preventing vision loss and blindness in high-risk patients. AI-assisted screening technologies are especially useful in areas with limited access to ophthalmologists [21].

2.5 AI in Cardiology

Artificial intelligence is transforming cardiology by improving the identification of arrhythmias, heart failure, and coronary artery disease. Machine learning models examine electrocardiograms (ECGs), echocardiograms, and patient histories to detect problems that would be missed by human interpretation [22]. AI-driven risk assessment models can also forecast an individual's risk of acquiring cardiovascular diseases based on lifestyle factors, genetic predisposition, and clinical history, allowing for preventive measures [23].

2.6 AI for Infectious Disease Detection

AI has helped diagnose infectious disorders like COVID-19, tuberculosis, and sepsis. AI algorithms evaluate chest X-rays, blood testing, and patient symptoms to accurately identify illnesses. In the case of COVID-19, AI-assisted CT scan analysis has helped uncover virus-associated pneumonia patterns, allowing for speedier triage and treatment [24].

2.7 AI for Multimodal Diagnosis

Beyond single-modality diagnosis, artificial intelligence is increasingly being utilized for multimodal data integration, which combines imaging, genetic, laboratory, and clinical data to increase diagnostic accuracy [25]. AI-powered platforms can combine genetic sequencing data with radiological and pathological results to deliver more accurate and tailored diagnosis, especially in complicated diseases such as cancer and neurodegenerative disorders [13].

Table: 1Different aspects of AI in medical diagnosis

3. AI for Treatment Planning and Personalized Medicine

AI allows personalised treatment techniques based on patient-specific data, allowing healthcare professionals to customize therapy to particular need. By evaluating genetic information, medical history, imaging data, and real-time patient monitoring, AI improves decision-making, drug selection, and treatment outcomes.

3.1 Artificial Intelligence in Oncology Treatment

Cancer treatment has advanced tremendously thanks to AI-powered customized medicine. AI algorithms examine genetic mutations and tumour biomarkers to determine the most effective targeted therapy for each patient [8]. Systems such as IBM Watson for Oncology analyze massive volumes of medical literature and clinical trial data to recommend the best chemotherapy, immunotherapy, or radiation treatments [34]. Furthermore, AI can forecast how a patient's tumour would respond to specific treatments, avoiding unwanted side effects and increasing survival rates.

3.2 Artificial Intelligence in Cardiovascular Disease Management

AI plays an important role in cardiology, assisting with treatment planning for illnesses such as hypertension, heart failure, and coronary artery disease. AI-powered risk assessment models examine patient data, such as electrocardiograms (ECGs), imaging results, and wearable device readings, to prescribe tailored lifestyle changes, drugs, or surgical procedures. AI also helps to optimize pharmaceutical regimens by predicting how patients will react to various medications based on their genetic and metabolic characteristics [35&36].

3.3 Artificial intelligence in neurology and neurodegenerative disorders

Artificial intelligence has altered the treatment of neurological ailments such as Alzheimer's disease, Parkinson's disease, epilepsy, and multiple sclerosis. AI can anticipate illness development and recommend tailored treatment solutions based on brain imaging, genetic variables, and cognitive assessments [37]. In epilepsy, AI-powered seizure prediction models aid in the adjustment of anticonvulsant therapy, lowering seizure frequency and enhancing patient quality of life. Similarly, AI assists in determining the most effective treatment combinations for Parkinson's patients depending on disease stage and symptom severity [38].

3.4 Artificial Intelligence for Endocrinology and Diabetes Management

AI-powered solutions in diabetes management track blood glucose levels, predict swings, and provide individualized insulin dosages. AI-powered continuous glucose monitoring (CGM) systems analyze real-time blood sugar data and make recommendations to help prevent hyperglycemia and hypoglycemia. Additionally, AI-powered digital health platforms provide food and lifestyle suggestions based on specific patient patterns, which improves diabetes control and reduces complications [39, 40].

3.5 AI for Drug Discovery and Precision Medicine

AI is speeding up drug discovery by evaluating massive datasets of chemical structures, clinical trial findings, and patient responses to find potential new medicines [41]. AlphaFold and other AI-powered platforms predict protein structures, which aids in the development of tailored medicines for diseases such as cancer, Alzheimer's, and autoimmune disorders [42]. AI is also enhancing pharmacogenomics, which allows clinicians to prescribe the appropriate medication and dosage based on a patient's genetic profile, reducing adverse drug reactions [43].

3.6 Artificial intelligence in robotic surgery and minimally invasive procedures

AI-assisted robotic surgery, like the da Vinci Surgical System, improves precision, lowers complications, and shortens recuperation periods [44]. Artificial intelligence-powered robotic devices help surgeons perform laparoscopic, cardiac, and orthopaedic surgeries, increasing results by offering real-time guidance, detecting anomalies, and decreasing human error [45]. These technologies enable less invasive surgeries, which result in shorter hospital stays and speedier recovery for patients [46].

4. AI for Healthcare Operations and Patient Management

AI streamlines healthcare operations, improving patient care efficiency by automating administrative activities, improving workflow management, and optimizing resource allocation [47]. AI plays an important role in increasing hospital efficiency, lowering physician burnout, and improving patient outcomes (Table 2).

4.1 Artificial Intelligence in Electronic Health Records (EHR) Management

Managing electronic health records is a time-consuming task that frequently stresses healthcare providers. AI-powered systems use natural language processing (NLP) and machine learning techniques to automate data entry, access patient information, and summarize medical notes [48]. AI can extract crucial insights from unstructured clinical data, resulting in better decision-making and fewer documentation errors. Furthermore, predictive analytics in EHRs aids in the identification of high-risk patients, allowing for early intervention to avoid consequences [49].

4.2 AI-powered Chatbots & Virtual Assistants

AI-powered chatbots and virtual assistants offer 24-hour patient support, handling duties such as appointment scheduling, prescription reminders, and symptom assessment [50]. Chatbots such as Ada Health and Buoy Health employ artificial intelligence to assess patient symptoms and provide preliminary diagnoses before sending users to relevant healthcare services [51]. Virtual assistants also help healthcare providers by transcribing medical notes, making reminders, and collecting patient information, which reduces administrative workload [52].

4.3 AI for Hospital Resource Management

Hospitals use artificial intelligence to optimize bed allocation, personnel scheduling, and medical supply chain management. AI algorithms forecast patient admission rates, helping hospitals manage resources more efficiently and avoid overcrowding [53]. AI also helps to manage medical inventories by forecasting demand for pharmaceuticals, surgical instruments, and diagnostic tools, so avoiding shortages and decreasing waste [54].

4.4 AI and Predictive Analytics for Patient Care

AI-powered predictive analytics can help identify patients who are at risk of complications, readmissions, or disease progression [55]. Machine learning algorithms scan patient data to discover early warning signals of illnesses including sepsis, heart failure, and post-surgical infections, allowing doctors to treat before the symptoms develop [56]. AI-powered remote monitoring systems capture real-time patient data from wearable devices, allowing doctors to detect anomalies and intervene early [57].

4.5 AI for Medical Billing and Fraud Detection

AI improves the accuracy of medical billing and insurance claims processing by minimizing errors and detecting fraudulent activity [58]. AI-powered technologies automate coding and billing, assuring accurate documentation and adherence to insurance standards. Additionally, AI finds irregularities in billing patterns, indicating potential fraud or overbilling, allowing healthcare businesses to save money while maintaining financial integrity [59].

4.6 AI for Telemedicine and Remote Healthcare

Telemedicine has grown tremendously because to AI-powered systems that enables virtual consultations, remote diagnostics, and patient monitoring [60]. AI-powered telehealth services let doctors diagnose illnesses using patient complaints, voice analysis, and facial expressions. Remote patient monitoring devices with AI, continuously measure vital signs, glucose levels, and heart rate, allowing clinicians to provide real-time assistance and avoid unnecessary hospitalizations [61].

Table: 2 AI Applications in Healthcare Operations and Patient Management

5. Challenges and ethical considerations

Despite its transformative promise, AI in medicine confronts a number of obstacles, including data privacy concerns, bias in AI models, a lack of transparency, and legal difficulties [67]. Ethical considerations, such as ensuring fairness in AI-powered healthcare decisions and maintaining patient trust, are also important. Addressing these concerns is critical to the responsible implementation of AI in medical practice [68].

5.1 Data Protection and Security

AI in healthcare is based on enormous volumes of sensitive patient data, such as medical records, genetic information, and imaging scans [69]. Ensuring data privacy and security is a significant concern, as breaches could disclose personal health information (PHI), resulting in identity theft or misuse [70]. Compliance with data protection regulations, such as HIPAA (Health Insurance Portability and Accountability Act) in the United States and GDPR (General Data Protection Regulation) in Europe, is required to protect patient data [71]. To ensure patient confidentiality, AI systems must use strong encryption, secure data storage, and anonymization techniques.

5.2 Bias and Fairness of AI Models

The data on which AI models are trained determines their effectiveness. If training datasets are insufficiently varied or representative, AI systems may develop biases, resulting in discrepancies in patient outcomes [72]. For example, AI models trained exclusively on data from specific racial or socioeconomic groups may underperform when applied to underrepresented communities, resulting in incorrect diagnoses or treatment recommendations [73]. To provide equitable healthcare for all patients, AI bias must be addressed using diversified datasets, continuous model validation, and bias mitigation measures [74].

5.3 Explainability and Trust in AI Decision-Making

Many AI models, particularly deep learning systems, operate as black boxes, making it impossible to comprehend how they reach specific conclusions [75]. The lack of explainability undermines trust between healthcare practitioners and patients, as clinicians must justify treatment recommendations. Creating explainable AI (XAI) models with explicit reasons for their judgments is critical to gaining acceptability and adoption in clinical practice [76]. SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) are two techniques that contribute to increased AI transparency [77].

5.4 Integration with Existing Healthcare Systems

Many healthcare facilities use legacy IT systems and infrastructure that may not be compatible with AI technologies. Integrating AI into existing EHR systems, hospital workflows, and medical imaging platforms necessitates major investments in software, training, and infrastructure changes [78]. Additionally, interoperability concerns across different healthcare systems complicate AI deployment. Standardizing AI implementation and creating interoperable frameworks can help to easy integration [79].

5.5 Regulatory and Legal Challenges

The use of AI in medicine creates significant legal and regulatory issues. Current medical rules are frequently not built to support AI-driven diagnosis and treatment suggestions, making it difficult to develop guidelines for AI approval and supervision [80]. Regulatory authorities like as the United States Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are developing frameworks to ensure AI-powered healthcare solutions meet safety, efficacy, and ethical requirements [81, 82]. However, striking a balance between innovation and patient safety remains a major challenge.

5.6 Ethical Considerations for AI-Driven Healthcare

The ethical implications of AI in medicine go beyond privacy and bias to include concerns like patient autonomy, informed consent, and liability [83]. For example, if an AI system provides an inaccurate diagnosis or treatment prescription, deciding who is to blame—the AI developer, the healthcare professional, or the institution—becomes a legal and ethical quandary [84]. Furthermore, AI should supplement, not replace, human decision-making, ensuring that physicians maintain control over patient care. Ethical AI guidelines should be developed to guide the responsible application of AI in healthcare [85].

6. Future Prospects for AI in Medicine

The future of AI in medicine seems bright, with advances in deep learning, predictive analytics, and robotic systems continuing to change healthcare. As AI technologies advance, they will improve early disease detection, precision medicine, and patient-centered treatment. Future AI technologies are projected to emphasize more integration with wearable health devices, genomics, and real-time monitoring systems, enabling proactive and tailored healthcare interventions (Figure 1).

Fig: 1 Future Prospect for AI in Medicine

6.1 AI for Preventive Medicine and Early Diagnosis

AI-powered predictive analytics will play an important role in preventative medicine by identifying those who are at risk for diseases before they develop symptoms. AI models can use genetic data, lifestyle factors, and medical history to forecast the risk of acquiring cancer, diabetes, and cardiovascular disease. Early diagnosis using AI-powered imaging and biomarker analysis will allow for timely interventions, increasing survival rates and lowering healthcare expenditures [86].

6.2 AI-Driven Drug Discovery and Development

AI is poised to transform pharmaceutical research by drastically decreasing the time and expense required for medication discovery [87]. AI-powered systems can examine millions of chemical compounds, forecast drug-target interactions, and select viable drug candidates for clinical trials [88]. AlphaFold, a technology that accurately predicts protein structures will help to accelerate the development of new medicines for diseases like Alzheimer's, uncommon genetic disorders, and infectious diseases [89].

6.3 AI for Robotics and Automated Surgery

The future of robotic-assisted surgery will see increasingly powerful AI-powered robotic devices that improve surgical precision, shorten recuperation times, and reduce hazards [90]. Autonomous surgical robots will aid human surgeons in complex procedures, improving precision and reducing fatigue-related errors. AI will also improve remote robotic surgeries, enabling specialists to do treatments on patients in remote regions via 5G-enabled telemedicine [91&92].

6.4 AI and Brain-Computer Interfaces (BCI)

Emerging research in brain-computer interfaces (BCIs), when paired with AI, has the potential to revolutionize therapy for neurological illnesses, paralysis, and cognitive impairment [93]. AI-powered BCIs can decipher brain impulses, allowing people with disabilities to communicate, use prosthetic limbs, and potentially regain motor function. These breakthroughs will considerably improve rehabilitation and assistive technologies for those suffering from spinal cord injuries or neurodegenerative disorders [94, 95].

6.5 AI-Powered Digital Health Assistants and Personalized Care

AI-powered virtual health assistants will grow increasingly advanced, providing real-time medical advice, mental health support, and lifestyle guidance [96]. AI-powered chatbots will work alongside wearable gadgets and smart home technology to deliver continuous health monitoring, medication reminders, and emergency alarms. These developments will provide real-time AI-driven feedback to assist people manage chronic illnesses such as diabetes and hypertension [97].

6.6 AI for Global Healthcare Accessibility

AI has the ability to close healthcare gaps in low-income and rural areas by offering AI-powered diagnostics, telemedicine services, and automated health evaluations [98]. AI-powered mobile applications and portable diagnostic instruments will allow for remote consultations, early disease identification, and improved access to healthcare specialists in underprivileged areas [99]. This could help to address global health disparities and improve healthcare access worldwide.

Artificial intelligence is transforming medicine by improving diagnosis accuracy, treatment planning, and healthcare operations. AI-powered advances in radiology and pathology, as well as personalized medicine and robotic surgery, improve patient outcomes, eliminate human errors, and optimize clinical workflows. Furthermore, AI-powered chatbots, virtual assistants, and predictive analytics improve patient management and early disease identification. Despite its enormous promise, AI in healthcare confronts obstacles such as data protection, algorithmic bias, transparency, and system integration. Ensuring conformity with ethical principles and regulatory standards is critical to its appropriate execution. To overcome these challenges, AI researchers, healthcare practitioners, and legislators must work together to develop fair, transparent, and secure AI models. Precision medicine, robotic-assisted treatment, wearable health monitoring, and global healthcare accessibility will all benefit from AI in the future. With continued improvements and ethical considerations, AI is poised to reshape current medicine, making it more efficient, personalized, and accessible globally

1. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education. 2023 Sep 22;23(1):689.
2. Khalifa M, Albadawy M. AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update. 2024 Mar 5:100146.
3. Sarella PN, Mangam VT. AI-driven natural language processing in healthcare: transforming patient-provider communication. Indian Journal of Pharmacy Practice. 2024;17(1).
4. Van Leeuwen KG, de Rooij M, Schalekamp S, van Ginneken B, Rutten MJ. How does artificial intelligence in radiology improve efficiency and health outcomes?. Pediatric radiology. 2022 Oct 12:1-7.
5. Rasool S, Ali M, Shahroz HM, Hussain HK, Gill AY. Innovations in AI-powered healthcare: Transforming cancer treatment with innovative methods. BULLET: Jurnal Multidisiplin Ilmu. 2024;3(1):118-28.
6. Försch S, Klauschen F, Hufnagl P, Roth W. Artificial intelligence in pathology. Deutsches Ärzteblatt International. 2021 Mar 26;118(12):199.
7. Ali MT, Ali U, Ali S, Tanveer H. Transforming cardiac care: AI and machine learning innovations. International Journal of Multidisciplinary Research and Growth Evaluation. 2024 Jul.
8. Sherani AM, Khan M, Qayyum MU, Hussain HK. Synergizing AI and Healthcare: Pioneering advances in cancer medicine for personalized treatment. International Journal of Multidisciplinary Sciences and Arts. 2024 Feb 4;3(2):270-7.
9. Rashid M, Sharma M. AI‐Assisted Diagnosis and Treatment Planning—A Discussion of How AI Can Assist Healthcare Professionals in Making More Accurate Diagnoses and Treatment Plans for Diseases. AI in Disease Detection: Advancements and Applications. 2025 Jan 8:313-36.
10. Kassem H, Beevi AA, Basheer S, Lutfi G, Cheikh Ismail L, Papandreou D. Investigation and Assessment of AI’s Role in Nutrition—An Updated Narrative Review of the Evidence. Nutrients. 2025 Jan 5;17(1):190.
11. Wah JN. Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation. Journal of Robotic Surgery. 2025 Dec;19(1):1-5.
12. Albahri AS, Duhaim AM, Fadhel MA, Alnoor A, Baqer NS, Alzubaidi L, Albahri OS, Alamoodi AH, Bai J, Salhi A, Santamaría J. A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion. 2023 Aug 1;96:156-91.
13. Oyeniyi J, Oluwaseyi P. Emerging trends in AI-powered medical imaging: enhancing diagnostic accuracy and treatment decisions. International Journal of Enhanced Research In Science Technology & Engineering. 2024;13:2319-7463.
14. Oladele OK. AI-Powered Medical Imaging: A Comprehensive Review of Applications, Benefits, and Challenges.
15. Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information. 2025 Mar 3;16(3):195.
16. Broggi G, Maniaci A, Lentini M, Palicelli A, Zanelli M, Zizzo M, Koufopoulos N, Salzano S, Mazzucchelli M, Caltabiano R. Artificial Intelligence in Head and Neck Cancer Diagnosis: A Comprehensive Review with Emphasis on Radiomics, Histopathological, and Molecular Applications. Cancers. 2024 Oct 27;16(21):3623.
17. Ahmad Z, Rahim S, Zubair M, Abdul-Ghafar J. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagnostic pathology. 2021 Dec;16:1-6.
18. Li Pomi F, Papa V, Borgia F, Vaccaro M, Pioggia G, Gangemi S. Artificial intelligence: a snapshot of its application in chronic inflammatory and autoimmune skin diseases. Life. 2024 Apr 16;14(4):516.
19. Almustafa KM. Predictive modeling and optimization in dermatology: Machine learning for skin disease classification. Computers in Biology and Medicine. 2025 May 1;189:109946.
20. Vilela LF, Cabral NO, Destefani AC, Destefani VC. Harnessing the power of artificial intelligence for early detection and management of diabetic retinopathy, age-related macular degeneration, and glaucoma: A narrative review of deep learning applications in ophthalmology. Revista Ibero-Americana de Humanidades, Ciências e Educação. 2024 Aug 26;10(8):3311-20.
21. Chen W, Li R, Yu Q, Xu A, Feng Y, Wang R, Zhao L, Lin Z, Yang Y, Lin D, Wu X. Early detection of visual impairment in young children using a smartphone-based deep learning system. Nature medicine. 2023 Feb;29(2):493-503.
22. Yasmin F, Shah SM, Naeem A, Shujauddin SM, Jabeen A, Kazmi S, Siddiqui SA, Kumar P, Salman S, Hassan SA, Dasari C. Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future. Reviews in cardiovascular medicine. 2021 Dec 22;22(4):1095-113.
23. Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, Russak A, Zhao S, Levin MA, Freeman RS, Charney AW. Using deep-learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram. Cardiovascular Imaging. 2022 Mar 1;15(3):395-410.
24. Rabaan AA, Bakhrebah MA, Alotaibi J, Natto ZS, Alkhaibari RS, Alawad E, Alshammari HM, Alwarthan S, Alhajri M, Almogbel MS, Aljohani MH. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review. Journal of Infection and Public Health. 2023 Nov 1;16(11):1837-47.
25. Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nature medicine. 2022 Sep;28(9):1773-84.
26. Suppadungsuk S, Thongprayoon C, Miao J, Krisanapan P, Qureshi F, Kashani K, Cheungpasitporn W. Exploring the potential of chatbots in critical care nephrology. Medicines. 2023 Oct 20;10(10):58.
27. Froiz-Míguez I, Fraga-Lamas P, Fernández-CaraméS TM. Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A System for Galician and Mobile Opportunistic Scenarios. IEEE Access. 2023 Jun 14;11:63623-49.
28. Liu F, Chen D, Zhou J, Xu F. A review of driver fatigue detection and its advances on the use of RGB-D camera and deep learning. Engineering Applications of Artificial Intelligence. 2022 Nov 1;116:105399.
29. Majumder S, Dey N. AI-empowered knowledge management. Springer; 2022 Feb 23.
30. Tsvetanov F. Integrating AI technologies into remote monitoring patient systems. Engineering Proceedings. 2024 Aug 20;70(1):54.
31. Devaraj SM. Enterprise System Modernization: A Roadmap To Digital Success. International Journal Of Research In Computer Applications And Information Technology (Ijrcait). 2024 Nov 15;7(2):1238-51.
32. Pickhardt PJ, Correale L, Hassan C. AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis. Abdominal Radiology. 2023 Mar;48(3):1181-98.
33. Mulukuntla S, Pamulaparthyvenkata S. Realizing the Potential of AI in Improving Health Outcomes: Strategies for Effective Implementation. ESP Journal of Engineering and Technology Advancements. 2022;2(3):32-40.
34. Liu Y, Huo X, Li Q, Li Y, Shen G, Wang M, Ren D, Zhao F, Liu Z, Zhao J, Liu X. Watson for oncology decision system for treatment consistency study in breast cancer. Clinical and Experimental Medicine. 2023 Sep;23(5):1649-57.
35. Gala D, Behl H, Shah M, Makaryus AN. The role of artificial intelligence in improving patient outcomes and future of healthcare delivery in cardiology: a narrative review of the literature. InHealthcare 2024 Feb 16 (Vol. 12, No. 4, p. 481). MDPI.
36. Vora LK, Gholap AD, Jetha K, Thakur RR, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023 Jul 10;15(7):1916.
37. Kalani M, Anjankar A. Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment. Cureus. 2024 Jun 5;16(6).
38. Onciul R, Tataru CI, Dumitru AV, Crivoi C, Serban M, Covache-Busuioc RA, Radoi MP, Toader C. Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications. Journal of Clinical Medicine. 2025 Jan 16;14(2):550.
39. Khanam A, Masoodi FS, Bamhdi A. From data to insights: Leveraging machine learning for diabetes management. InA Biologist s Guide to Artificial Intelligence 2024 Jan 1 (pp. 97-123). Academic Press.
40. Iftikhar M, Saqib M, Qayyum SN, Asmat R, Mumtaz H, Rehan M, Ullah I, Ud-Din I, Noori S, Khan M, Rehman E. Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review. Annals of Medicine and Surgery. 2024 Sep 1;86(9):5334-42.
41. Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Development Research. 2023 Dec;84(8):1652-63.
42. Rehman AU, Li M, Wu B, Ali Y, Rasheed S, Shaheen S, Liu X, Luo R, Zhang J. Role of artificial intelligence in revolutionizing drug discovery. Fundamental Research. 2024 May 9.
43. Haga SB. Artificial intelligence, medications, pharmacogenomics, and ethics. Pharmacogenomics. 2024 Oct 12;25(14-15):611-22.
44. Guo C, He Y, Shi Z, Wang L. Artificial intelligence in surgical medicine: a brief review. Annals of Medicine and Surgery. 2025:10-97.
45. Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial Intelligence in Surgery: A Systematic Review of Use and Validation. Journal of Clinical Medicine. 2024 Nov 24;13(23):7108.
46. Abbasi N, Hussain HK. Integration of artificial intelligence and smart technology: AI-driven robotics in surgery: precision and efficiency. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023. 2024 Aug 21;5(1):381-90.
47. Machireddy JR. Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI. Hong Kong Journal of AI and Medicine. 2022 May 26;2(1):10-36.
48. Sarella PN, Mangam VT. AI-driven natural language processing in healthcare: transforming patient-provider communication. Indian Journal of Pharmacy Practice. 2024;17(1).
49. Saadat S, Khalilizad Darounkolaei M, Qorbani M, Hemmat A, Hariri S. Enhancing Clinical Documentation with AI: Reducing Errors, Improving Interoperability, and Supporting Real-Time Note-Taking. InfoScience Trends. 2025 Jan 14;2(1):1-3.
50. Huseynov F. Chatbots in digital marketing: Enhanced customer experience and reduced customer service costs. InContemporary approaches of digital marketing and the role of machine intelligence 2023 (pp. 46-72). IGI Global.
51. Xu L, Sanders L, Li K, Chow JC. Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR cancer. 2021 Nov 29;7(4):e27850.
52. Chavali D, Dhiman VK, Katari SC. AI-Powered Virtual Health Assistants: Transforming Patient Engagement Through Virtual Nursing. Int. J. Pharm. Sci. 2024;2:613-24.
53. Shamsi M. Integrating Artificial Intelligence for Prediction and Optimization in Hospital Management Systems (Case study: Iranian Hospital in Dubai). Journal of Business and Future Economy. 2024 Dec 28;1(4):1-9.
54. Hiziroglu OA. Implementation of Artificial Intelligence for the Healthcare Supply Chain: Prospects and Challenges. The Impact of Artificial Intelligence on Healthcare Industry.:208-26.
55. Hossain S, Ahmed A, Khadka U, Sarkar S, Khan N. AI-driven Predictive Analytics, Healthcare Outcomes, Cost Reduction, Machine Learning, Patient Monitoring. AIJMR-Advanced International Journal of Multidisciplinary Research. 2024 Oct 22;2(5).
56. El-Sherbini AH, Hassan Virk HU, Wang Z, Glicksberg BS, Krittanawong C. Machine-learning-based prediction modelling in primary care: state-of-the-art review. Ai. 2023 May 23;4(2):437-60.
57. Bhambri P, Khang A. Managing and monitoring patient's healthcare using AI and IoT technologies. InDriving Smart Medical Diagnosis Through AI-Powered Technologies and Applications 2024 (pp. 1-23). IGI Global.
58. Dey R, Roy A, Akter J, Mishra A, Sarkar M. AI-driven machine learning for fraud detection and risk management in US healthcare billing and insurance. Journal of Computer Science and Technology Studies. 2025 Feb 12;7(1):188-98.
59. Kilanko V. The transformative potential of artificial intelligence in medical billing: a global perspective. Int J Sci Adv. 2023;4(3):346.
60. Vashishth TK, Sharma V, Kumar S, Verma N, Vidyant S, Kaushik S. The Integration of AI in Telemedicine Transforming Healthcare Delivery and Patient Outcomes. InAI-Driven Personalized Healthcare Solutions 2025 (pp. 71-98). IGI Global Scientific Publishing.
61. Bacha A, Shah HH. AI-Powered Virtual Health Assistants: Transforming Patient Care and Engagement. Global Insights in Artificial Intelligence and Computing. 2025 Jan 23;1(1):15-30.
62. Gupta J, Raychaudhuri N, Lee M. Conversational artificial intelligence in healthcare. InMachine Learning and Autonomous Systems: Proceedings of ICMLAS 2021 2022 Feb 10 (pp. 449-457). Singapore: Springer Nature Singapore.
63. Uddin MK. A Review of Utilizing Natural Language Processing and AI For Advanced Data Visualization in Real-Time Analytics. Global Mainstream Journal. 2024 Apr 20;1(4):10-62304.
64. Sone K, Tanimoto S, Toyohara Y, Taguchi A, Miyamoto Y, Mori M, Iriyama T, Wada-Hiraike O, Osuga Y. Evolution of a surgical system using deep learning in minimally invasive surgery. Biomedical reports. 2023 May 30;19(1):45.
65. Sivakumar SA. Predictive Analytics for Demand Response Management with AI. Acta Energetica. 2024 Jul 17(02):12-22.
66. SNIGDHA EZ, HOSSAIN MR, MAHABUB S. AI-Powered Healthcare Tracker Development: Advancing Real-Time Patient Monitoring and Predictive Analytics Through Data-Driven Intelligence. Journal of Computer Science and Technology Studies. 2023 Dec 25;5(4):229-39.
67. Williamson SM, Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Applied Sciences. 2024 Jan 12;14(2):675.
68. Nasir S, Khan RA, Bai S. Ethical framework for harnessing the power of AI in healthcare and beyond. IEEE Access. 2024 Feb 26;12:31014-35.
69. Ahmadi A, RabieNezhad Ganji N. AI-driven medical innovations: transforming healthcare through data intelligence. International Journal of BioLife Sciences (IJBLS). 2023 Oct 1;2(2):132-42.
70. Vikash BS. Exploring Challenges Faced by Information Technology Security Managers in Implementing Risk Management Framework to Protect Protected Health Information and Personally Identifiable Information (Doctoral dissertation, Northcentral University).
71. Purike E, Judijanto L. COMPARISON OF DATA PROTECTION POLICIES IN EUROPE AND THE UNITED STATES: DIFFERENT LEGAL APPROACHES. Jurnal Komunikasi. 2025 Mar 22;3(3):115-22.
72. Celi LA, Cellini J, Charpignon ML, Dee EC, Dernoncourt F, Eber R, Mitchell WG, Moukheiber L, Schirmer J, Situ J, Paguio J. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digital Health. 2022 Mar 31;1(3):e0000022.
73. Vaidya A, Chen RJ, Williamson DF, Song AH, Jaume G, Yang Y, Hartvigsen T, Dyer EC, Lu MY, Lipkova J, Shaban M. Demographic bias in misdiagnosis by computational pathology models. Nature Medicine. 2024 Apr;30(4):1174-90.
74. Nazer LH, Zatarah R, Waldrip S, Ke JX, Moukheiber M, Khanna AK, Hicklen RS, Moukheiber L, Moukheiber D, Ma H, Mathur P. Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS digital health. 2023 Jun 22;2(6):e0000278.
75. Hassija V, Chamola V, Mahapatra A, Singal A, Goel D, Huang K, Scardapane S, Spinelli I, Mahmud M, Hussain A. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation. 2024 Jan;16(1):45-74.
76. Mahto MK. Explainable artificial intelligence: Fundamentals, Approaches, Challenges, XAI Evaluation, and Validation. InExplainable Artificial Intelligence for Autonomous Vehicles 2025 (pp. 25-49). CRC Press.
77. Kalusivalingam AK, Sharma A, Patel N, Singh V. Leveraging SHAP and LIME for Enhanced Explainability in AI-Driven Diagnostic Systems. International Journal of AI and ML. 2021 Feb 15;2(3).
78. Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artificial Intelligence in Medicine. 2024 May 1;151:102861.
79. Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artificial Intelligence in Medicine. 2024 May 1;151:102861.
80. Mennella C, Maniscalco U, De Pietro G, Esposito M. Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon. 2024 Feb 29;10(4).
81. Ajmal CS, Yerram S, Abishek V, Nizam VM, Aglave G, Patnam JD, Raghuvanshi RS, Srivastava S. Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making. The AAPS Journal. 2025 Jan 7;27(1):22.
82. Mirakhori F, Niazi SK. Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective. Pharmaceuticals. 2025 Jan 3;18(1):47.
83. Naik N, Hameed BM, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, Shetty S. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility?. Frontiers in surgery. 2022 Mar 14;9:862322.
84. Zhang J, Zhang ZM. Ethics and governance of trustworthy medical artificial intelligence. BMC medical informatics and decision making. 2023 Jan 13;23(1):7.
85. Formosa P, Rogers W, Griep Y, Bankins S, Richards D. Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation contexts. Computers in Human Behavior. 2022 Aug 1;133:107296.
86. Modi K, Singh I, Kumar Y. A comprehensive analysis of artificial intelligence techniques for the prediction and prognosis of lifestyle diseases. Archives of Computational Methods in Engineering. 2023 Nov;30(8):4733-56.
87. Saini JP, Thakur A, Yadav D. AI driven Innovations in Pharmaceuticals: Optimizing Drug Discovery and Industry Operations. RSC Pharmaceutics. 2025.
88. Abbas MK, Rassam A, Karamshahi F, Abunora R, Abouseada M. The role of AI in drug discovery. Chembiochem. 2024 Jul 15;25(14):e202300816.
89. Ozaybi MQ, Madkhali AN, Alhazmi MA, Faqihi HM, Alanazi MM, Siraj WH, Zalah AH, Khormi MM, Al Salem AM, Mashragi TQ, Alotaibi AN. The Role of Artificial Intelligence in Drug Discovery and Development. Egyptian Journal of Chemistry. 2024 Dec 1;67(13):1541-7.
90. Iftikhar M, Saqib M, Zareen M, Mumtaz H. Artificial intelligence: revolutionizing robotic surgery. Annals of Medicine and Surgery. 2024 Sep 1;86(9):5401-9.
91. Shah JK, Yadav A, Hopko SK, Mehta RK, Pagilla PR. Robot adaptation under operator cognitive fatigue using reinforcement learning. In2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2023 Aug 28 (pp. 1467-1474). IEEE.
92. Georgiou KE, Georgiou E, Satava RM. 5G use in healthcare: the future is present. JSLS: Journal of the Society of Laparoscopic & Robotic Surgeons. 2021 Oct;25(4):e2021-00064.
93. Awuah WA, Ahluwalia A, Darko K, Sanker V, Tan JK, Pearl TO, Ben-Jaafar A, Ranganathan S, Aderinto N, Mehta A, Shah MH. Bridging minds and machines: The recent advances of brain-computer interfaces in neurological and neurosurgical applications. World Neurosurgery. 2024 May 22.
94. Kumar R, Tanaka A. AI-Driven Solutions for Neuroprosthetics: Bridging Neural Activity and Machine Learning in BCIs. Eastern European Journal for Multidisciplinary Research. 2024 Oct 29;3(2):72-8.
95. Gupta R. AI-based technologies, challenges, and solutions for neurorehabilitation: A systematic mapping. InComputational Intelligence and Deep Learning Methods for Neuro-Rehabilitation Applications 2024 Jan 1 (pp. 1-25). Academic Press.
96. Bachina L, Kanagala A. Health revolution: AI-powered patient engagement. International Journal of Chemical and Biochemical Sciences. 2023;24(5):2023.
97. Anisha SA, Sen A, Bain C. Evaluating the potential and pitfalls of AI-powered conversational agents as humanlike virtual health carers in the remote management of noncommunicable diseases: scoping review. Journal of Medical Internet Research. 2024 Jul 16;26:e56114.
98. Mbanugo OJ. AI-Enhanced Telemedicine: A Common-Sense Approach to Chronic Disease Management and a Tool to Bridging the Gap in Healthcare Disparities. Department of Healthcare Management & Informatics, Coles College of Business, Kennesaw State University, Georgia, USA.
99. Emeihe EV, Nwankwo EI, Ajegbile MD, Olaboye JA, Maha CC. The impact of artificial intelligence on early diagnosis of chronic diseases in rural areas. Int J Biol Pharm Res Updates. 2024;5(8):1828-54.