AI-Driven Chatbots and Virtual Assistants in Pharmaceutical Care: A Comprehensive Review

Abstract

Artificial intelligence (AI) has brought about a transformation in healthcare, with chatbots and virtual assistants emerging as key components in pharmaceutical care. These AI-powered instruments improve patient involvement, offer medication reminders, facilitate access to drug information, and aid pharmacists in their decision-making processes. This review aims to examine the various applications, advantages, obstacles, and future trajectories of AI-driven chatbots and virtual assistants within the pharmacy sector. The incorporation of AI into pharmaceutical care has led to better medication adherence, fewer human errors, and greater availability of healthcare information. Nevertheless, issues like data privacy, regulatory considerations, and the necessity for ongoing enhancement of AI models persist. Overcoming these challenges will be vital for the extensive implementation and success of AI in pharmaceutical environments.

Keywords

Introduction

Artificial intelligence (AI) involves machines engineered to replicate human intellectual processes, particularly within computer systems. These processes encompass self-correction, logical reasoning to reach potential or definite conclusions, and comprehension through information acquisition and application. As AI reaches its current peak, its applications continue to broaden, with chatbots serving as a prime illustration of its advancing capabilities. AI has significantly impacted healthcare, fostering the creation of advanced technologies that improve patient care. Specifically, AI-driven chatbots and virtual assistants play a vital role in pharmaceutical care by boosting patient engagement, ensuring medication adherence, and streamlining healthcare delivery. These tools provide ongoing support, personalized interactions, and efficient handling of medication-related inquiries. This review delves into the applications, advantages, challenges, and future directions of AI-driven chatbots and virtual assistants in pharmaceutical care.

For instance, personalized medicine, a therapeutic approach that customizes treatments to each patient's unique characteristics, has recently gained considerable attention. This progress necessitates adaptations in the tools and procedures employed in pharmacy practice. The integration of artificial intelligence (AI), a transformative technology capable of simulating human intelligence, could accelerate this evolution. AI offers numerous applications in the medical field, including data analysis, disease prediction, and personalized treatment recommendations, all of which have the potential to revolutionize various aspects of patient care.

The pharmaceutical industry is currently reaping substantial benefits from emerging technologies such as augmented reality, blockchain, and others, leading to enhanced patient care and satisfaction. Digital innovation, design, and distribution are becoming increasingly central to the pharmaceutical sector. Cutting-edge digital technologies are being implemented at every stage of the medical lifecycle, from drug discovery to marketing and patient outcomes. Given the widespread adoption of these technologies across various industries and their significant impact on customer engagement and satisfaction, the pharmaceutical sector has also embraced them.

For example, AI-driven drug discovery is replacing human resources in many pharmaceutical companies due to its efficiency, cost-effectiveness, and continuous operational capability. Similarly, AI-powered chatbots are becoming popular within the pharmaceutical industry, providing tailored information to help consumers make informed decisions. Equipping pharmacies with comprehensive patient data improves their capacity to select the most effective medication and treatment strategies.

Concurrently, rapid advancements in computing power enable businesses to generate and process vast quantities of data using advanced machine learning and deep learning algorithms, which form the bedrock of AI. Despite years of digitalizing medical data from the pharmaceutical sector and subsequent human analysis, there has been no notable improvement in industrial efficiency. Artificial intelligence (AI) enhances this process by enabling real-time patient data processing, optimizing clinical trial candidate selection, and facilitating seamless information sharing among patients, physicians, healthcare providers, and insurance companies. Additionally, AI offers potential solutions to long-standing industry challenges, such as the high costs and lengthy timelines associated with drug development. With its ability to interpret and analyze text, voice notes, and visual data more quickly and accurately than humans or other technologies, AI presents a transformative opportunity for the pharmaceutical sector.

2. Literature Review

The growing body of academic work supports the incorporation of AI into pharmaceutical care. Research consistently shows the effectiveness of AI-driven chatbots in boosting medication adherence through tailored reminders and educational programs. Studies also indicate that virtual assistants can improve patient engagement by offering immediate support and drug information. One study published in the International Journal of Pharmaceutical Sciences reported a 30% increase in patient satisfaction when AI-powered virtual assistants were utilized. Similarly, research in the Journal of Medical Internet Research demonstrated that AI chatbots effectively reduced medication errors by providing real-time drug interaction checks.

• Singh et al. cite_start observed that AI-powered chatbots have been successful in managing chronic diseases, enhancing medication adherence, and decreasing hospital readmissions.
• Zhang et al. cite_start documented a 30% reduction in administrative tasks within pharmacies that implemented AI-integrated systems.
• Lee & Kim underscored AI's critical function in clinical decision support, aiding pharmacists with drug interaction assessments, contraindications, and recommendations for alternative therapies.
• Chen & Wang (2022) highlighted the importance of Natural Language Processing (NLP) in improving the conversational abilities of virtual assistants.
• Gupta & Mehta (2022) identified key obstacles in AI implementation, such as data privacy concerns, regulatory issues, and limitations in model accuracy, emphasizing the need to address these barriers for secure and effective AI deployment in pharmacy practice.
• Sharma et al. stressed the necessity of continuous AI model training and validation, advocating for pharmacist oversight to ensure AI reliability, particularly in clinical decision-making.
• Patel et. al. investigated the integration of AI with Electronic Health Records (EHRs) for personalized medication management.
• Wang & Li et. al. proposed that advancements in NLP models could significantly improve interactions between patients and pharmacists.
• Teo ZL et. al. pointed out that Diabetic Retinopathy (DR), a preventable leading cause of adult blindness, faces screening challenges due to a shortage of ophthalmologists.
• Andrzej Grzybowski et al. reviewed the development of various AI-driven systems designed to facilitate DR screening.
• NICE Public Health Guidelines (2007) emphasized the importance of adherence in managing chronic diseases. Brar Prayaga R et al. showed how AI improves adherence rates and reduces instances of non-adherence.
• Augello A., Pilato et. al. introduced the U-report chatbot for medication management, while Lee D. et al. presented Casper, another AI-driven chatbot aimed at assisting patients with adherence and treatment plans.
• Ameri A. et. al. discussed the role of tele pharmacy services in enhancing pharmaceutical care. Okolo CA et. al. explored how AI innovations support and improve tele pharmacy operations, increasing both accessibility and efficiency.

3. Benefits of AI Integration in Pharmaceutical Care:

3.1 Improved Medication Adherence:

The World Health Organization (WHO) defines adherence as the extent to which an individual's actions—such as taking medication, following a prescribed diet, or making lifestyle adjustments—align with healthcare provider recommendations. This widely recognized definition emphasizes the importance of active patient participation and effective communication between patients and healthcare professionals. Despite numerous efforts to enhance adherence, medication nonadherence continues to be a significant challenge for researchers and healthcare providers. In developed countries, nonadherence rates for chronic disease treatments typically fall between 30% and 50%, with even higher rates observed in developing nations.

Given the complexity and diverse factors influencing medication adherence, a multifaceted approach is essential to improve adherence rates. Artificial intelligence (AI) has emerged as a promising tool in these interventions. AI applications in medicine can be broadly categorized into two main types: virtual AI, which encompasses informatics and deep learning, and physical AI, such as robot-assisted systems. AI-powered chatbots, in particular, play a crucial role by offering personalized medication reminders and educational support, encouraging patients to adhere to their prescribed treatment plans.

3.2 Enhanced Accessibility:

The growing aging population has led to an increase in various diseases, including diabetes, neurological disorders like dementia, Alzheimer's disease, and Parkinson's disease, cardiovascular conditions such as ischemic cardiomyopathy, cerebrovascular disease, and hypertensive heart disease, as well as chronic respiratory disorders like asthma and chronic obstructive pulmonary disease. AI-driven tools are vital in improving healthcare accessibility by providing easy access to reliable drug information, especially in remote or underserved areas, thereby helping to bridge gaps in medical care.

Two significant societal trends—digitalization and population aging—are profoundly impacting healthcare systems. As aging populations drive up healthcare and pension costs, digitalization becomes crucial in managing these rising expenses. Digital technology offers more efficient ways to organize healthcare and public services, optimizing time and resources for self-care among employees. Consequently, digital transformation is viewed as a cost-effective solution to the challenges posed by an aging population. Beyond financial support, digital health technologies empower older adults to maintain their independence at home for extended periods, improving their quality of life and lessening the burden on healthcare systems.

3.3 Reduction in Human Error:

AI enhances pharmaceutical care by assisting in medication management and performing real-time interaction checks, thereby minimizing the risk of human errors.

3.4 Prescription Validation:

AI plays a critical role in verifying prescriptions by cross-referencing them with patient medical records and legal regulations. These advanced systems detect errors such as incorrect dosages, duplicate prescriptions, and inappropriate medication choices. Research indicates that AI-powered prescription validation significantly improves patient safety, reducing processing errors by 35%.

3.5 Simplified Operations:

AI-powered solutions enhance tele pharmacy operations by automating repetitive tasks and seamlessly integrating with electronic health records (EHRs). For instance, virtual assistants handle routine patient inquiries, allowing pharmacists to concentrate on more complex clinical responsibilities. Furthermore, these technologies facilitate real-time communication between patients and pharmacists, ensuring efficient and effective service delivery.

3.6 Conservation of Time:

Since adherence is influenced by various complex factors, patients’ self-reported adherence may not always be accurate in clinical practice, and physicians often lack the time to thoroughly investigate the underlying causes of non-adherence. AI-assisted technology can help address this challenge by enabling healthcare providers to allocate more time to essential clinical tasks and engage in deeper patient discussions about diseases and treatment plans.

4. Applications of AI-Driven Chatbots and Virtual Assistants

4.1 Patient Engagement and Support:

AI-driven chatbots enhance patient engagement by providing instant assistance, answering medication-related questions, and offering personalized health information. They can schedule appointments, send medication reminders, and monitor patient adherence. By facilitating continuous communication between patients and healthcare providers, these tools improve patient satisfaction and health outcomes. A study conducted by HealthTech Magazine reported a 25% improvement in patient satisfaction scores following the implementation of AI-driven engagement tools.

A busy lifestyle is a common excuse for medication non-adherence, leading to a decline in the drug’s therapeutic effect due to the failure to achieve a steady-state concentration. While pharmaceutical companies have introduced solutions such as smart pill bottles and mobile apps, many of these have not effectively met individual patient needs. However, AI has shown a positive impact by enabling personalized patient engagement, offering a more effective approach to improving adherence.

An example of AI-driven solutions for medication adherence is Allazo Health, which utilizes artificial intelligence to assess individual patient adherence and help pharmaceutical companies enhance patient support programs to overcome adherence barriers.

Another example is Diabetic Retinopathy (DR), a common complication of Diabetes Mellitus that is a leading cause of preventable blindness in adults. Due to a shortage of ophthalmologists, meeting DR screening demands is challenging, negatively impacting both public health and economic outcomes. Again, AI plays a crucial role by providing high-accuracy screening with reduced human errors, improving early detection and patient outcomes.

Figure 1

• IDx-DR system: Also known as Luminetics Core, this is the first FDA-approved autonomous AI tool for diagnosing retinal diseases. It analyzes data from multiple biomarker detectors to assess image quality and detect abnormalities like neovascularization, hemorrhages, and exudates. Designed for individuals without a prior diabetic retinopathy diagnosis, it identifies cases beyond mild severity using the Topcon TRC-NW400, a high-resolution retinal camera.
• Retmarker DR: The Retmarker DR software, a CE-marked Class IIa medical device developed in Portugal, has been used for local diabetic retinopathy (DR) screening. In 2011, it was integrated into an existing human-grader-based DR screening program in central Portugal.
• The Bosch DR algorithm: Bosch's recently announced autonomous DR screening solution utilizes the Bosch Mobile Eye Care fundus camera. The captured images are analyzed by a convolutional neural network-based AI program, which classifies the results as either disease present or no disease.
• Retinalyze: Retinalyze is a cloud-based program that provides automated screening for diabetic retinopathy (DR) and, more recently, glaucoma. It is a CE-marked Class I device that allows secure image submission via an end-to-end encrypted website. Its effectiveness was first scientifically documented in 2003 through studies on patient cohorts of 137, 100, and 83 individuals.

4.2 Medication Management:

Virtual assistants play a crucial role in medication management by providing dosage instructions, highlighting potential side effects, and alerting users to possible drug interactions. By integrating with electronic health records (EHRs), these tools offer personalized medication guidance and enhance patient safety. They also help prevent medication errors by identifying harmful interactions in real time.

4.3 Dose of Drugs with Age:

Drug intake is generally determined based on a patient’s age and weight, as medication dosages are not one-size-fits-all. To ensure appropriate dosing, chatbots can be programmed to adjust medication recommendations according to the recipient’s age, helping to optimize safety and effectiveness.

• UNICEF (U-Report Bot): UNICEF employs multinational, non-profit child development chatbots to help individuals in impoverished regions express their most urgent needs. One such initiative, the U-Report bot, is designed to gather large-scale data through user participation. These users, known as "U-Reporters," engage in periodic polls on critical social issues, providing valuable insights that UNICEF leverages to shape future policies. For instance, in Liberia, U-Report conducted a survey on the exploitation of students by teachers, revealing that over 86% of the 13,000 respondents had experienced coercion in exchange for better grades. In response, UNICEF collaborated with Liberia’s Ministry of Education to address and eliminate this unethical practice.
• Casper Insomnobot 3000: Assisting People with Sleep Disorders: One of the most challenging aspects of insomnia is the profound sense of loneliness that comes from being awake while the rest of the world sleeps. Many insomniacs struggle with intrusive thoughts and anxieties during these late hours. To address this, Casper developed Insomnobot 3000, a conversational agent designed to provide companionship and engagement when it is inconvenient to interact with others. This chatbot offers a comforting presence, helping users feel less isolated during sleepless nights.
• ALICE: In 1995, Dr. Richard Wallace introduced ALICE (Artificial Linguistic Internet Computer Entity), a chatbot designed to simulate human conversation. Despite its outdated codebase, ALICE remains a stable and responsive AI, often providing unexpectedly insightful answers.
• Chatbots are transforming healthcare by improving patient communication with doctors and medical organizations. AI-powered bots can analyze symptoms, suggest possible conditions, provide medication details, and direct users to the right healthcare provider. In emergencies, they enable quick responses and faster solutions. However, their effectiveness depends on accurate condition identification and reliable medical data.

4.4 Support for Pharmacists

• AI in Hospital Pharmacies: AI-driven platforms improve communication between pharmacists and physicians by providing real-time updates on medications, drug interactions, and dosage recommendations. These tools enhance patient safety and therapeutic efficacy by analyzing vast patient data and clinical guidelines. Example: IBM Watson for Oncology suggests personalized cancer treatments by analyzing medical histories and clinical trial data. Example: CASTER, an AI tool, predicts drug interactions and adverse reactions by analyzing chemical structures using deep learning.
• AI in Community Pharmacies: AI enhances supply chain management by predicting drug demand based on historical sales, health trends, and external factors like disease outbreaks. It also evaluates vendors based on cost, reliability, and product quality to optimize inventory. Additionally, AI improves automated dispensing systems (ADSs) by learning from errors and refining dispensing accuracy. It aids in public health monitoring by detecting disease trends and alerting pharmacists to potential outbreaks. AI also plays a role in addressing health disparities by analyzing demographic and socioeconomic data, linking health outcomes with factors like zip codes to improve care accessibility.

5. Challenges and Considerations:

5.1 Data Privacy and Security: AI in healthcare raises concerns about patient data privacy and security. Ensuring compliance with regulations and implementing robust security measures is essential. AI-driven monitoring of patient data may require additional training for pharmacists to integrate AI into their practice effectively.

5.2 Regulatory and Ethical Issues: The deployment of AI in healthcare must navigate complex regulations to ensure safety and efficacy. Ethical concerns include data ownership, racial and ethnic disparities in research participation, and the risk of AI-driven decision-making overriding patient needs. Transparency is critical—AI-based telepharmacy systems must obtain patient consent before collecting data and clearly communicate how information is stored, shared, and used.

5.3 Limitations of AI Models: AI models can provide inaccurate or biased information, requiring continuous validation. Challenges include:

• Limited Digital Infrastructure: Rural areas often lack reliable internet access, restricting tele pharmacy use.
• Shortage of Skilled Personnel: AI-driven pharmacy services require trained professionals, which are scarce in low-resource settings.
• Algorithmic Bias: AI models trained on unrepresentative datasets may not meet the needs of underserved populations, leading to disparities in pharmaceutical recommendations.

6. Future Directions:

Advancements in Natural Language Processing (NLP), EHR integration, patient education, and regulatory frameworks are crucial for the continued development of AI-driven chatbots in pharmaceutical care. Further research is needed to enhance AI accuracy and establish standardized regulations for safe implementation. Future studies should explore AI integration with healthcare systems, such as telemedicine and EHRs, to improve care coordination, patient outcomes, and cost efficiency. Additionally, ethical concerns—including algorithmic bias, data security, and job displacement—must be proactively addressed to ensure AI’s responsible use in pharmacy. Healthcare AI research should prioritize ethics, efficacy, transparency, and reliability. While most AI research in healthcare has focused on pediatrics, further studies are needed to assess AI’s effectiveness in addressing age-related diseases and geriatric care.

CONCLUSION

AI has the potential to revolutionize pharmacy, benefiting both pharmacists and patients. It enhances medication safety, streamlines drug development, and improves patient outcomes while reducing healthcare costs through increased efficiency and error minimization. AI-driven chatbots and virtual assistants are reshaping pharmaceutical care by improving patient engagement, medication management, and pharmacist support. However, challenges such as data privacy, regulatory compliance, and AI model accuracy must be addressed. Despite these hurdles, the advantages of AI in pharmacy far outweigh its limitations. With proper implementation and oversight, AI can pave the way for a more efficient, safe, and patient-centric healthcare.

REFERENCES

1. Setiaji, B., & Wibowo, F. W., Chatbot using a knowledge in database: human-to-machine conversation modeling. In 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), page no.72-77, 2016.
2. Ahmad Z. AI Meslamani, Applications of AI in Pharmacy practice: a look at hospital and community settings, Journal of Medical Economics, volume 26, page no. 1081-1084, 2023.
3. Trenfield, S. J. Awad, A. McCoubrey, L. E. Elbadawi, M. Goyanes, A. Gaisford, S, Basit, Advancing pharmacy and healthcare with virtual digital technologies. Advanced Drug Delivery Reviews, 182, 2022.
4. Jing Ziyi, Tachakorn Wongkumchai, Chulalux Soprakan, Ntapat Worapongpat, Assessing the role of AI-Powered chatbots in improving customer experience in pharmaceutical e-commerce business in Bejing, volume 8, issue1, page no. 1-18, June 2024.
5. M. Chui, Artificial intelligence the next digital frontier? McKinsey and Company Global Institute 47, page no. 3–6, 2017.
6. P.V. Henstock, Artificial intelligence for pharma: time for internal investment, Trends Pharmacol. Sci. 40 (8), page no.543–546, 2019.
7. S.H. Kumar, D. Talasila, M.P. Gowrav, H.V. Gangadharappa, Adaptations of pharma 4.0 from industry 4.0, Drug Invent. Today 14 (3) (2020).
8. S. Harrer, P. Shah, B. Antony, J. Hu, Artificial intelligence for clinical trial design, Trends Pharmacol. Sci. 40 (8), page no.577–591, 2019
9. Davin Cho., Adherence intelligence is the new AI, Allazohealth, January 2021
10. Teo ZL et al., Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology,128(11):1580-1591, may 2021
11. Andrzej Grzybowski, Piotr Brona, Gilbert Lim, Paisan Ruamviboonsuk et al., Artificial intelligence for diabetes retinopathy screening: a review, The Royal college of ophthalmologists, page no. 451-460,2020
12. Abramoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy, JAMA Ophthalmol,131:351–7, 2013
13. Van der Heijden AA, Abramoff MD, Verbraak F, van Hecke MV, Liem A, Nijpels G, Validation of automated screening for referable diabetic retinopathy with the IDx?DR device in the Hoorn Diabetes Care System Acta Ophthalmol (Copenh), 96:63–8, 2018
14. Ribeiro L, Oliveira CM, Neves C, Ramos JD, Ferreira H, Cunha-Vaz J. Screening for diabetic retinopathy in the central region ofPortugal. Added value of automated ‘disease/no disease’ grading Ophthalmologica, 233:96–103, 2015
15. Bawankar P, Shanbhag N, Dhawan B, Palsule A, Kumar D,Chandel S, et al. Sensitivity and specificity of automated analysisof single-field non-mydriatic fundus photographs by Bosch DR Algorithm—Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy. PLoSONE, 2017
16. Larsen N, Godt J, Grunkin M, Lund-Andersen H, Larsen M.Automated detection of diabetic retinopathy in a fundus photo-graphic screening population. Invest Ophthalmol Vis Sci, 44:767–71, 2003
17. Mishra, S. K., Bharti, N. Dr. Vdoc: A Medical Chatbot that Acts as a Virtual Doctor. Research & Reviews: Journal of Medical Science and Technology, 6(3), 16-20, 2018
18. Chetan Bulla, Chinmay Parushetti, et al., A review of AI based medical assistant chatbot, Research and applications of web development and design, HBRP Publication, volume 3, issue 3, page no. 1-14, 2020
19. Augello, A., Pilato, G., Machi, A., & Gaglio, An approach to enhance chatbot semantic power and maintainability: experiences within the FRASI project. In 2012 IEEE Sixth International Conference on Semantic Computing, page no. 186-193, september 2012
20. Lee, D., Oh, K. J., & Choi, H. J, The chatbot feels you-a counseling service using emotional response generation. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), page no. 437-440, February 2017
21. Bibault, J. E., Chaix, B., Nectoux, P., Pienkowski, A., Guillemasé, A., & Brouard, Healthcare ex Machina: Are conversational agents ready for prime time in oncology?. Clinical and translational radiation oncology, 16, page no. 55-59, 2019
22. Atiyah, A., Jusoh, S., & Almajali, An efficient search for context-based chatbots. In 2018 8th International Conference on Computer Science and Information Technology (CSIT), page no.125-130, July 2018
23. Langebrake C, Hilgarth H. Clinical pharmacists’ interventions in a German university hospital. Pharm World Sci.,32(2), page no.194–199, 2010
24. Zhou N, Zhang CT, Lv HY, et al. Concordance study between IBM watson for oncology and clinical practice for patients with cancer in China. Oncologist, 24(6), page no.812–819, June 2019.
25. Huang K, Xiao C, Hoang TN, et al. CASTER: predicting DrugInteractions with Chemical Substructure Representation, 2019.
26. Bohr A, Memarzadeh K. The rise of artificial intelligence in health-care applications. In: Artificial intelligence in healthcare. Singapore: Springer, page no. 25–60, 2020.
27. Raza MA, Aziz S, Noreen M, et al. Artificial intelligence (AI) in pharmacy: an overview of innovations. Inov Pharm,13(2):13, 2022.
28. Venkatramanan S, Sadilek A, Fadikar A, et al. Forecasting influenza activity using machine-learned mobility map. Nat Commun.
29. SabateEAdherence to Long-Term Therapies: Evidence for Action Geneva World Health Organization, 2003
30. NICE Public Health Guidelines Behavior Change at Population, Community and Individual Levels London NICE Public Health Guidelines, 2007
31. Brown MT, Bussell JK. Medication adherence: WHO cares? Mayo Clin Proc., 86:304–14, 2011
32. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 69S:S36–40, 2017.
33. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke, 48:1416–9, 2017
34. Brar Prayaga R, Agrawal R, Nguyen B, Jeong EW, Noble HK, Paster A, et al. Impact of social determinants of health and demographics on refill requests by medicare patients using a conversational artificial intelligence text messaging solution: cross-sectional study. JMIR Mhealth Uhealth, 2019
35. Z. Cai, J. Zhang, H. Li Selenium, aging and aging-related diseases Aging Clin. Exp. Res., page no.1035-1047, 2019
36. T. Turja, L. Van Aerschot, T. Särkikoski, A. Oksanen Finnish healthcare professionals’ attitudes towards robots: reflections on a population sample Nurs. Open, 5 (3), page no. 300-309, 2018
37. M. Ra, M. Mk, V. Am Older adults can use technology: why healthcare professionals must overcome ageism in digital health Transl. Behav. Med., 12 (12), December 2022
38. Ameri A, Salmanizadeh F, Keshvardoost S, Bahaadinbeigy K. Investigating pharmacists’ views on telepharmacy: prioritizing key relationships, barriers, and benefits. Journal of Pharmacy Technology, 36(5):171-8, October 2020
39. Okolo CA, Arowoogun JO, Chidi R, Adeniyi AO. Telemedicine's role in transforming healthcare delivery in the pharmaceutical industry: A systematic review. World Journal of Advanced Research and Reviews, 21(2):1836-56, 2024
40. Saeed H, Martini ND, Scahill S. Exploring Telepharmacy: A Bibliometric Analysis of Past Research and Future Directions. Research in Social and Administrative Pharmacy, May 2024
41. Wathoni N, Lestari K, Iftinan GN, Rahayu SA, Nurlatifah A, Khairinisa MA, Elamin KM. Knowledge, Perception, and Readiness of Indonesian Pharmacists for the Implementation of Telepharmacy-Based Pharmaceutical Services in Indonesia. Integrated Pharmacy Research and Practice, 31:213-25, December 2023
42. Sarasmita MA, Sudarma IW, Jaya MK, Irham LM, Susanty S. Telepharmacy Implementation to Support Pharmaceutical Care Services during the COVID-19 Pandemic: A Scoping Review. The Canadian Journal of Hospital Pharmacy, January 2024
43. Garetto R, Allegranti I, Cancellieri S, Coscarelli S, Ferretti F, Nico MP. Ethical and legal challenges of telemedicine implementation in rural areas. In Information and Communication Technology (ICT) Frameworks in Telehealth Cham: Springer International Publishing, page no. 31-60, 23 August 2022
44. Gellad WF, Grenard JL, Marcum ZA. A systematic review of barriers to medication adherence in the elderly: looking beyond cost and regimen complexity. Am J Geriatr Pharmacother , 2011
45. Brown MT, Bussell JK. Medication adherence: WHO cares? Mayo Clin Proc.,86:304–14, 2011
46. Lewis Deborah, EdD RN, CDE. Computer-based approaches to patient education: a review of the literature. J Am Med Inf Assoc., 6(4):272–282, July 1999
47. Reddy Sandeep, Allan Sonia, Coghlan Simon, Cooper Paul. A governance model for the application of AI in health care. J Am Med Inf Assoc., 27(Issue 3):491–497, march 2020
48. Weisberg EM, Chu LC, Fishman EK. The first use of artificial intelligence (AI) in the ER: triage not diagnosis. Emerg Radiol, 27:361–366, 2020
49. Wang M, Xia C, Huang L, et al. Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation. Lancet Digital Health, 2020
50. Osama Khan, Mohd Parvez, et al., The future of pharmacy: how AI is revolutionizing the industry, Intelligent pharmacy, published by Elsevier B.V, page no. 32-40, April 2023
51. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 69S:S36–40, 2017
52. Rubeis G. The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care. Arch Gerontol Geriatr, 91:104186, 2020 doi: 10.1016/j.archger.2020.104186
53. Dang JHT, Rodriguez EM, Luque JS, Erwin DO, Meade CD, Chen MS. Engaging diverse  populations about biospecimen donation for cancer research. J. Commun. Genet. 5:313–27, 2014
54. Rubeis G. The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care. Arch Gerontol Geriatr, 91:104186, 2020
55. Al Meslamani AZ. Technical and regulatory challenges of digital health implementation in developing countries, Journal of Medical Economics, 26(1):1057-60, December 2023
56. Omaghomi TT, Arowoogun JO, Akomolafe O, Odilibe IP, Elufioye OA. Telemedicine in rural Africa: A review of accessibility and impact. World Journal of Advanced Research and Reviews, 21(2):421-31, 2024
57. McCarthy C, Bateman Jr MT, Henderson T, Jean R, Evans R. Adoption of telepharmacy within a community health center: a focus on clinical pharmacy services. Journal of the American College of Clinical Pharmacy, 4(8):924-33, August 2021
58. Ekundayo F. Machine learning for chronic kidney disease progression modelling: Leveraging data science to optimize patient management. World J Adv Res Rev, 24(03):453–475. 2024. doi:10.30574/wjarr.2024.24.3.373
59. Adam Wright, McEvoy Dustin S, Aaron Skye, et al. Structured override reasons fordrug-drug interaction alerts in electronic health records. J Am Med Inf Assoc.,26(Issue 10):934–942, October 2019. https://doi.org/10.1093/jamia/ocz033.
60. Vollmer S, Mateen BA, Bohner G, Kira?ly FJ, Ghani R, Jonsson P, et al. Machinelearning and artificial intelligence research for patient benefit: 20 criticalquestions on transparency, replicability, ethics, and effectiveness. BMJ, 368:l692, 2020. doi: 10.1136/bmj.l6927
61. Merkel S, Kucharski A. Participatory design in gerontechnology: a systematicliterature review. Gerontologist, 59:e16–25, 2019. doi: 10.1093/geront/gny034
62. Chen LK. Gerontechnology and artificial intelligence:better care for older people. Arch Gerontol Geriatr, 91:104252, 2020. doi: 10.1016/j.archger.2020.104252.