Waving the A.I wand: Enchanting Diagnostics, Empowering Pharmacy, and Hospital Wizardry

Sufiyan Ansari*, Dr. G.J. Khan

J.I.I.U’S Ali-Allana College of Pharmacy Akkalkuwa, Dist.- Nandurbar (425415) Maharashtra, India.

Abstract: Artificial Intelligence (AI) has emerged to be a powerful force, captivating the world with its influence in various fields of science. In the medical field, AI has yet to reach its full potential. To throw light on this matter, review the current advancements, provide researched based insights, and predict some future outcomes, we have reviewed several articles related to AI and its uses in the medical field. We thus, have broadly classified the data into three major groups namely: diagnostics, pharmacy, and hospital management. This review article delves into the mesmerizing impact of AI in transforming healthcare practices and unlocking a realm of possibilities where sci-fi isn’t tagged with fiction but with reality. In the realm of diagnostics, using AI powered data sets in disease detection and diagnosis, accuracy of diagnosis has evidently increased.   Keywords: Artificial Intelligence, AI, AI diagnostics, drug discovery AI, hospital management.

Corresponding Author: Sufiyan Ansari Email ID: ansarisufiyan038@gmail.com    Contact: 9323158918

Article History Received:        15/09/2023 Accepted:        25/10/2023 Published:       01/11/2023

 

 

 

 

 

 

 

 

 

 

 

 

  INTRODUCTION:

Artificial Intelligence (AI) has emerged as an impressive force across various domains, reshaping industries and societies. In the medical field, the profound impact of AI has become increasingly clear, revolutionizing several classes of medical care. This review article aims to explore the extensive applications and advancements of AI in medicine, shedding light on the promising developments that have propelled AI into a tool that could potentially reshape the future of medicine.

Through a comprehensive analysis of literature, case studies, and all the news regarding recent advancements of AI, we aim to provide an insightful overview of the current state of AI implementation in the medical domain and its potential implications for the future of healthcare. To simplify, we have decided to review them based on their differentiation on its concerning different fields of expertise. The fields selected were Diagnostics, Pharma, and Management of hospitals, and other healthcare facilities. 

In diagnostics, AI-driven medical imaging analysis and predictive analytics have shown to improved disease detection, enabling early identification of abnormalities and facilitating more accurate diagnoses. The integration of AI with medical imaging technologies, such as MRI, CT, PET, and ultrasound, has yielded higher-resolution images and enhanced tissue differentiation, empowering healthcare providers with deeper insights into various pathologies. This could result in balancing the statistics towards favoring of AI and its potential applications.

Predictive analytics, fueled by AI algorithms, harnesses patient data to identify individuals at higher risk of specific diseases, by merely apply principles of statistics and facilitating timely interventions and preventive measures. Continuous monitoring through smart bands and wearables ensures real-time tracking of vital signs, empowering healthcare providers to make prompt decisions at the right moments, leading to improved patient outcomes and reduced hospital readmissions.

AI's influence in Pharma is an issue that needs to be addressed urgently, because it has revolutionized drug discovery and drug repurposing processes by accelerating the identification of potential drug candidates and reducing time and cost involved in bringing new medications to market; through statistical driven data processing models. Tailored medication based on pharmacogenomics data is an insightful approach, customizing treatments to individual patients, thereby maximizing therapeutic efficacy while minimizing adverse drug reactions due to dosage errors. 

 AI-powered systems conduct drug interaction checks and support pharmacovigilance efforts, ensuring medication safety and surveillance of adverse drug reactions. Automations can be done on the receiving unit by AI powered customer support chatbots. AI-driven inventory management have streamlined pharmaceutical operations, optimizing medication dispensing and supply chain management, while also preventing drug shortages. The implementation of AI-based fraud prevention systems safeguards patients from getting expense and health concerns due to fraudulent activities in various departments.

In the quest of perfecting Management of hospitals, and other healthcare facilities, AI serves as a powerful ally in optimizing administrative tasks, enabling efficient appointment scheduling, patient record management, and resource allocation. Again, AI-driven systems contribute to fraud detection, ensuring the integrity of healthcare transactions and preserving the financial well-being of patients and institutions alike. Inventory management supported by AI here, would help optimize the availability of essential medical supplies, preventing stockouts and minimizing wastage, as every transaction would be transparent. AI also analyzes medication prices, aiding in cost optimization strategies and enabling hospitals to procure medications at competitive prices without compromise on quality. Price modulation based on selfish reasons would fall with AI’s cost optimization.

In conclusion, AI's growing impact on diagnostics, pharmacy, and hospital management holds immense promise in elevating medicine to its core and the pharma industry. The symbiotic relationship between human expertise and AI-driven innovations opens new doors for enhancing the way we diagnose patients. By embracing the transformative potential of AI in medicine, we embark on a journey towards a future where AI augments human capabilities in medicine, and not where we are scared of its implications.

AI in Diagnostics:

The use of AI in the field of diagnosis can be a crucial tool to eliminate errors and ensure critical judgement and early detection of diseases. In improving the diagnostic aspect of the medical field, AI can be used in software-based detection of anomalies in medical imaging and image base diagnostics, as well as statistical interpretation of data sets of terminally ill patients or patients with a history of genetic conditions. Let us understand them separately based on research evidence: -

1.      AI-powered medical imaging:

Researchers have successfully applied AI in radiology to identify findings either detectable or not by the human eye. As AI became more popular and also more medical images than ever have been generated, these are good reason for radiomics to evolve rapidly. Radiomics is a novel approach for solving the issue of precision medicine. These researches have demonstrated a great potential of the role of AI in medical imaging. In fact, it has sparkled one of the ongoing discussions—will AI replace clinicians entirely? We believe it will not. ⁽²⁾

Analysis of brain MRI using machine learning has the potential to identify tissue changes reflective of early ischaemic stroke within a narrow time window from symptom onset with greater sensitivity than a human reader! Identification of subtle structural and functional cardiac abnormalities with important clinical correlation could also be accomplished by AI techniques, such as convolutional neural networks, when applied to echocardiography, the most common form of cardiovascular imaging. ⁽³⁾

Not just AI powered medical imaging, it is also important to understand the potential benefits of including AI in diagnostic practises. A study was conducted in 2022 by Mr. Wang on Deep Learning Techniques to Diagnose Lung Cancer included several reports on how AI shaped the diagnosis of lung cancer from medical imaging. By using AI in diagnosis, we can eliminate the potential of errors in human diagnosis as well as other issues such as delayed patient support due to unavailability of data. ⁽¹¹⁾

2.      Predictive analytics:

Predictive analytics typically involves several key steps, including data collection and preparation, data analysis, model development, model validation, and model deployment. Data is collected from various sources, cleaned and pre-processed to remove inconsistencies and errors, and then analysed to identify relevant patterns and trends. Statistical algorithms and machine learning techniques are used to develop predictive models based on the historical data, which can then be validated using different techniques to assess their accuracy and reliability. Once validated, these models can be deployed in real-world scenarios to make predictions about future events or outcomes. ⁽⁴⁾

AI-based models have the potential to improve the care for in-hospital patients and especially those deteriorating on the ward. Appropriate escalation in case of deterioration is possible by integration of these models into clinical practice with the potential for real-time risk assessment. This would allow for more appropriate allocation of resources including staff as shortages are increasing and staffing ratios are directly correlated with outcome. A study conducted in 2018 showed the use of AI powered data sets in recognition and early detection of skin diseases ^(5). This proves the point that AI powered analysis would be extremely beneficial in early detection of diseases and can be done easily by training AI in advanced data sets.

3.      Continuous Monitoring (Smart Bands):

AI-driven wearable devices, like smart bands, provide real-time data for a patient’s health status. They can collect and analyse various types of data continuously, assisting in monitoring vital signs and other health parameters. A study conducted by Harmon in 2023 highlighted how AI-driven wearables helped in detecting abnormal heart rhythms and predicting cardiovascular events. The study also focused on how training the AI on different data sets might improve accuracy and precision of diagnosis. ⁽⁶⁾

4.      Benefits of real-time data:

Real-time data from AI-driven wearables benefits both healthcare providers in providing timely insights, and patients by updating their status with their caretakers. It enables early intervention and improved treatment plans. A study was conducted demonstrated how real-time monitoring and remote patient monitoring or RPM devices are already approved by the FDA and has been on a decline in the market due to increased demand. In fact, the market for RPM devices is already booming in several European and American countries. ⁽⁷⁾

5.      Personalized counselling and patient engagement:

Personalized counselling using AI enhances patient engagement and adherence to treatment plans. A study conducted by John and his colleagues in 2022 in Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools showed comparative evidence that using AI powered systems enhanced the efficacy of patient engagement and provided a tailored approached in dealing personalized healthcare. ⁽⁸⁾

6.      Overall Adherence Monitoring:

AI powered software could effectively monitor patient adherence to medication regimens. Smart pill bottles and smartphone apps are common methods used for tracking medication intake. A study by Ellsworth proved that Advanced Smart Pill Bottle would be the best Adherence Intervention in Patients with HIV on Antiretroviral Treatment. The study showed higher adherence levels of these smart pill bottles rather than using on traditional ones. ⁽⁹⁾

Research by A. Babel in 2021 highlighted that improved adherence through AI-based interventions reduced hospitalization rates and overall healthcare costs. It even helped in reducing non communicable diseases as well as spreading the information on communicable diseases. ⁽¹⁰⁾

Thus, AI helps tremendously in diagnostic practices to ensure swift patient support and continuous monitoring of patients. It also helps patients understand the various aspects of their diseases as well as gain valuable information from it.

AI in Pharmacy and Patient Care:

AI has yet to revolutionize various aspects of the medical and pharmaceutical fields, including drug discovery, precision medicine, and patient safety. Here, we explored the impact of AI on these critical aspects of pharmacy practice, highlighting recent advancements and their potential implications.

1.      Drug Discovery & Drug Repurposing:

AI has significantly accelerated the drug discovery process by leveraging machine learning algorithms to analyses chemical compounds and predict potential drug candidates. High through put screening is a process in which drug candidates are screened to uncover potential candidates. Incorporating AI in this step would ensure that no candidate is discarded until thorough filtration.  An extensive study on Artificial intelligence in drug discovery and development by Miss Paul has demonstrated the effectiveness of AI in virtual high-throughput screening, enabling the identification of promising compounds with higher efficiency and reduced costs. ⁽¹²⁾

Additionally, AI has proved valuable in drug repurposing, A study was conducted by Mr. Prasad Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2, where he and his colleagues concluded that drug repurposing can be done using machine learning and neural networks. Through AI-driven analysis, researchers can identify potential candidates for repurposing, leading to shorter development timelines and cost-effective drug development. ⁽¹³⁾

2.      Precision Medicine: Tailored Medication (Pharmacogenomics):

The integration of AI and pharmacogenomics allows for personalized medication selection and dosages based on an individual’s genetic makeup, patient history, allergies, idiosyncrasies and much more. By utilizing such groups of data, artificial neural networks can predict how patients will respond to specific medications, leading to improved treatment outcomes, tailored medical approach and reduced ADRs and ADVs.  A study conducted by Mr. Schork on AI and personalized medicine, stated that the use of machine learning and neural networks would only increase efficiency in prescribing doses and other factors. It also throws light on the transition phase of not using AI to fully integrated machine learning units. ⁽¹⁴⁾

3.      Drug Interaction Checks:

AI can play a crucial role in detecting and preventing potential drug interactions, promoting patient safety if incorporated in diagnosis or tailored medications. Real-time decision support systems powered by AI algorithms would analyses medication orders and patient profiles to identify potential interactions beforehand, aiding healthcare professionals in making informed decisions regarding the health and safety of the patients. A comparative review article written by Mr. Khan, on “The future of pharmacy: How AI is revolutionizing the industry”, states that the use of AI network mapping could uncover important secrets regarding drug-drug interactions. ⁽¹⁵⁾

4.      Pharmacovigilance:

In pharmacovigilance, AI has proven to be instrumental in monitoring and reporting adverse drug reactions using dedicated software. AI algorithms can analyze vast amounts of healthcare data, triangulating data sets to enable early detection of potential safety issues and ensuring medication safety and regulatory compliance using important structured data sets. Miss Murali, in her research paper on Artificial intelligence in pharmacovigilance: Practical utility, has stated that the process of using AI in the detection and monitoring of drug-based interactions and pharmacovigilance has seen significant improvements. ⁽¹⁶⁾

5.      Work flow and management:

AI-powered robotic systems can revolutionize pharmacy workflows by streamlining medication dispensing and inventory management. Automation can help reduce errors, improves efficiency, and allows pharmacists to dedicate more time to patient care and reduce time required for manufacturing practices. A study by Mr. Kumar for AI in the pharma field reports that the usage of AI powered bots in manufacturing operations or during work flow management is considered effective and reliable. Furthermore, AI’s predictive capabilities can also help facilitate drug shortage management. By analyzing supply chain data and market trends, AI can help in preventing and managing drug shortages, ensuring accurate inventory control and uninterrupted patient access to essential medications. ⁽¹⁷⁾

AI in Hospital Management:

The integration of Artificial Intelligence (AI) in the medical and pharmaceutical fields has paved the way for transformative changes in hospital management as well. In this section, we delve into the role of AI in various aspects of hospital operations, exploring its potential to streamline administrative tasks, optimize inventory management, and enhance medication price optimization during structured data sets.

1.      Administrative Support:

For hospital administration, revolutionizing mundane tasks such as billing and medical record management is necessary to ensure reduction of error and fraud cases. By leveraging automation and machine learning algorithms, Neural networks can enhance operational efficiency, reducing paperwork, and improving accuracy in maintaining important information. This newfound efficiency not only saves time but also saves time of healthcare professionals like pharmacists to focus more on patient healthcare. The integration of AI in administrative tasks presents a promising avenue for boosting hospital productivity as well. A study conducted in 2021 published information on various types of hospitals around the world, and their use of AI in hospital management. For example, a Hospital of Bozen (Italy), utilizes an intelligent tool able to support the definition and scheduling of the different laboratory tests, medical examinations and hospitalization. ⁽¹⁸⁾

2.      Inventory Management:

AI’s application in hospital inventory management introduces a more proactive and data-driven approach. By analyzing usage patterns, supply chain information, and patient demand, AI can optimize inventory levels, ensuring the availability of essential medications and supplies. It can help in making conscious decisions regarding inventory and stocking of medicines. Moreover, AI-driven inventory management minimizes waste and reduces the occurrence of stockouts, promoting efficient resource utilization in enhancing patient experience. A study conducted in 2021 by Miss Chowdhary on AI in inventory management in hospitals have revealed that there is significant increase in efficiency in using neural networks. ⁽¹⁹⁾

3.      Medication Price Optimization:

Medication affordability and cost management are of paramount importance for both the hospital and patients. AI emerges as a promising tool in addressing these challenges by facilitating medication price optimization. Through AI-driven analyses, hospitals can compare drug prices, negotiate favorable terms with suppliers, and identify cost-saving opportunities. This strategic application of AI in medication price optimization has the potential to enhance medication accessibility and affordability. A study conducted by Mr. Khanna on Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment; states that they show tremendous cost savings using AI tools in this step. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explaining ability, and regulatory approvals. ⁽²⁰⁾

The integration of AI in hospital management holds immense promise in streamlining administrative tasks, combating fraud in these operations, optimizing inventory management, and enhancing medication price optimization. Neural networks can offer tangible benefits, ranging from improved operational efficiency and cost savings to enhanced patient care. As the field of AI continues to evolve, healthcare institutions must stay in accordance with technological advancements to fully harness the potential of AI in healthcare.

CONCLUSIONS:

As we conclude this this review article, we stand at the threshold of a future filled with possibility. AI’s boundless potential, when coupled with compassion and wisdom, empowers us to reach new heights in healthcare. Together, we can forge a path towards a world where AI-driven advancements empower healthcare as well as other spheres of life. The AI revolution is upon us, and we have the choice of whether we embrace its brilliance responsibly or fear its mystery. Let us unite in our endeavors to harness AI for the greater good, where healthcare becomes a transformative force of healing and compassion.

 

 

 

 

REFERENCES:

1.    Millions harmed each year. [Internet] Harvard T.H. Chan School of Public Health. [updated at 18-Sept-2013]. Available from: https://www.hsph.harvard.edu/news/press-releases/millions-harmed-each-year-from-unsafe-medical-care/

2.    Tang X. The role of artificial intelligence in medical imaging research. BJR Open. 2019 Nov 28;2(1):20190031. doi: 10.1259/bjro.20190031. PMID: 33178962; PMCID: PMC7594889.

3.    Ohad Oren, Bernard J Gersh, Deepak L Bhatt, Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints, The Lancet Digital Health, Volume 2, Issue 9, 2020, Pages e486-e488, ISSN 2589-7500, https://doi.org/10.1016/S2589-7500(20)30160-6.

4.    Božić, Velibor. (2023). AI and Predictive Analytics. 10.13140/RG.2.2.23798.47682.

5.    Akyeramfo-Sam, Samuel, Acheampong Addo Philip, Derrick Yeboah, Nancy Candy love Nartey and Isaac Kofi Nti. “A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks.” International Journal of Information Technology and Computer Science (2019): n. page.

6.    Harmon DM, Sherawat O, Mayanja M, Wight J, Noseworthy PA. Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation. Arrhythm Electrophysiol Rev. 2023 Apr 19;12:e12. doi: 10.15420/aer.2022.31. PMID: 37427304; PMCID: PMC10326669.

7.    Dubey A, Tiwari A. Artificial intelligence and remote patient monitoring in US healthcare market: a literature review. J Mark Access Health Policy. 2023 May 3;11(1):2205618. doi: 10.1080/20016689.2023.2205618. PMID: 37151736; PMCID: PMC10158563.

8.    Piette JD, Newman S, Krein SL, Marine N, Chen J, Williams DA, Edmond SN, Driscoll M, LaChappelle KM, Kerns RD, Maly M, Kim HM, Farris KB, Higgins DM, Buta E, Heapy AA. Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial. JAMA Intern Med. 2022 Sep 1;182(9):975-983. doi: 10.1001/jamainternmed.2022.3178. PMID: 35939288; PMCID: PMC9361183

9.    Ellsworth GB, Burke LA, Wells MT, Mishra S, Caffrey M, Liddle D, Madhava M, O'Neal C, Anderson PL, Bushman L, Ellison L, Stein J, Gulick RM. Randomized Pilot Study of an Advanced Smart-Pill Bottle as an Adherence Intervention in Patients with HIV on Antiretroviral Treatment. J Acquire Immune Deific Syndr. 2021 Jan 1;86(1):73-80. doi: 10.1097/QAI.0000000000002519. PMID: 33306564; PMCID: PMC7735215.

10.    Babel A, Taneja R, Mondello Malvestiti F, Monaco A, Donde S. Artificial Intelligence Solutions to Increase Medication Adherence in Patients with Non-communicable Diseases. Front Digit Health. 2021 Jun 29;3:669869. doi: 10.3389/fdgth.2021.669869. PMID: 34713142; PMCID: PMC8521858.

11.    Wang L. Deep Learning Techniques to Diagnose Lung Cancer. Cancers (Basel). 2022 Nov 13;14(22):5569. doi: 10.3390/cancers14225569. PMID: 36428662; PMCID: PMC9688236.

12.    Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80-93. doi: 10.1016/j.drudis.2020.10.010. Epub 2020 Oct 21. PMID: 33099022; PMCID: PMC7577280.

13.         Kartikay Prasad, Vijay Kumar, Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2, Current Research in Pharmacology and Drug Discovery, Volume 2, 2021, 100042, ISSN 2590-2571, https://doi.org/10.1016/j.crphar.2021.100042.

14.    Schork NJ. Artificial Intelligence and Personalized Medicine. Cancer Treat Res. 2019;178:265-283. doi: 10.1007/978-3-030-16391-4_11. PMID: 31209850; PMCID: PMC7580505.

15.         Osama Khan, Mohd Parvez, Pratibha Kumari, Samia Parvez, Shadab Ahmad, The future of pharmacy: How AI is revolutionizing the industry, Intelligent Pharmacy, Volume 1, Issue 1, 2023, Pages 32-40, ISSN 2949-866X,

16.         Murali K, Kaur S, Prakash A, Medhi B. Artificial intelligence in pharmacovigilance: Practical utility. Indian J Pharmacol. 2019 Nov-Dec;51(6):373-376. doi: 10.4103/ijp.IJP_814_19. Epub 2020 Jan 16. PMID: 32029958; PMCID: PMC6984023.

17.    Bhattamisra SK, Banerjee P, Gupta P, Mayuren J, Patra S, Candasamy M. Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data and Cognitive Computing. 2023; 7(1):10. https://doi.org/10.3390/bdcc7010010

18.    Klumpp M, Hintze M, Immonen M, Ródenas-Rigla F, Pilati F, Aparicio-Martínez F, Çelebi D, Liebig T, Jirstrand M, Urbann O, Hedman M, Lipponen JA, Bicciato S, Radan AP, Valdivieso B, Thronicke W, Gunopulos D, Delgado-Gonzalo R. Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals. Healthcare (Basel). 2021 Jul 29;9(8):961. doi: 10.3390/healthcare9080961. PMID: 34442098; PMCID: PMC8393951.

19.    Chowdhury, Tasnim. (2021). USE OF ARTIFICIAL INTELLIGENCE (AI) IN MANAGING INVENTORY OF MEDICINE IN PHARMACEUTICAL INDUSTRY. 13. 3-15.

20.    Khanna NN, Maindarkar MA, Viswanathan V, Fernandes JFE, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Kolluri R, Singh IM, Laird JR, Fatemi M, Alizad A, Saba L, Agarwal V, Sharma A, Teji JS, Al-Maini M, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Mohanty L, Sobel DW, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Kitas GD, Fouda MM, Chaturvedi S, Kalra MK, Suri JS. Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare (Basel). 2022 Dec 9;10(12):2493. doi: 10.3390/healthcare10122493. PMID: 36554017; PMCID: PMC9777836.