Investigating the Impact of Machine Learning in Pharmaceutical Industry

Nagaprasad, S.; Padmaja, D. Lakshmi; Qureshi, Yaser; Bangare, Sunil L.; Mishra, Manmohan; Mazumdar, Bireshwar Dass

doi:10.9734/jpri/2021/v33i46a32834

Cited by 10 publications

(5 citation statements)

References 30 publications

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“…Traditional medical practices frequently rely on broad principles that might not take into account the considerable variances in patients' reactions to treatments. Contrarily, personalized medicine tries to match therapies to people based on their unique traits (Nagaprasad et al, 2021). This method acknowledges that a person's response to a given drug or treatment may be greatly influenced by genetic factors.…”

Section: Results and Discussion Enhancing Medical Imaging: Radiology'...mentioning

confidence: 99%

AI'S Healing Touch: Examining Machine Learning's Transformative Effects On Healthcare

Husnain,

Rasool,

Saeed

et al. 2023

JWS

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In the realm of healthcare, artificial intelligence (AI) emerges as a transformative force, reshaping established practices and offering unprecedented advancements. This comprehensive analysis delves into the multifaceted ways AI is revolutionizing healthcare, focusing on its transformative capabilities, inherent challenges, and the crucial ethical complexities entwined in its application. The challenge lies in balancing transparency and accountability amid the intricate algorithms, particularly concerning the interpretability of AI-generated insights. The analysis explores ethical dilemmas tied to patient autonomy and the evolving responsibilities of healthcare providers. It advocates for open dialogue among AI systems, patients, and healthcare professionals, navigating the delicate balance between innovation and patient welfare. The article emphasizes the imperative for robust ethical frameworks and regulations governing AI implementation in healthcare. The comprehensive investigation concludes by exploring AI's potential applications in healthcare, envisioning improved medical procedures, drug discoveries, remote patient monitoring, and diagnostic enhancements. To harness AI's transformative power while safeguarding patient interests, collaboration between healthcare professionals, data scientists, policymakers, and ethicists is paramount. This abstract encapsulates the profound shifts AI has initiated in healthcare, underscoring the vital need to harness its potential while addressing the ethical and regulatory complexities arising with its integration. Ultimately, it portrays a holistic view of AI's evolving role in healthcare, highlighting its potential to revolutionize patient care, medical practices, and the entire healthcare landscape.

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Section: Results and Discussion Enhancing Medical Imaging: Radiology'...mentioning

confidence: 99%

AI'S Healing Touch: Examining Machine Learning's Transformative Effects On Healthcare

Husnain,

Rasool,

Saeed

et al. 2023

JWS

View full text Add to dashboard Cite

show abstract

“…The application of AI and ML in digitizing pharmaceutical research also shows significant progress, with the potential for routine use in the future (Nagaprasad et al, 2021). However, research also notes challenges in implementing ML in manufacturing industries, such as model complexity and lack of relevant data (Weichert et al, 2019).…”

Section: Arif Rahman Alhakimimentioning

confidence: 99%

Potential Integration Of Machine Learning Algorithm And Manufacturing Execution System In The Lean Six Sigma Method To Improve Operational Excellence At ABC Farma Company

Alhakimi

2024

Eduvest

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This research focuses on the implementation of Operational Excellence (OE) in the pharmaceutical industry, especially in companies that transformed from PT Askes (Persero) to the Health Insurance Organizing Agency (BPJS). One approach adopted is Lean Six Sigma (LSS) which is supported by the Manufacturing Execution System (MES) to improve the efficiency and quality of the production process. This research takes a case study on product A with a focus on improvements to hardness issues. Through the Define, Measure, Analyze, Improve, and Control (DMAIC) stages, traditional methods and advanced analysis based on Machine Learning (ML) algorithms are used to improve production processes. The results showed success in achieving significant improvements in the productivity index and quality of product A production processes, making a valuable contribution in the context of Operational Excellence research in the pharmaceutical industry. The LSS method, which has been modified by integrating with ML algorithms and MES, provides productivity aspects consisting of LT, PCE and TD, as well as quality aspects consisting of better Pp, Ppk and sigma levels. The modified LSS method also provides the potential for COGM savings of 2,66 billion Rupiah and reduces the risk of production process failure to less than 200 batches for every 1 million production batches. Based on this, it can be concluded that the development of the Lean Six Sigma method which has been integrated with ML and MES algorithms has the positive potential to increase OE at ABC Farma.

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“…There are concerns that larger businesses are applying the software applications that are there now, which is setting up the digital future of this market. Recognized drug firms work with healthcare professionals in artificial intelligence (AI) to apply AI to general drug discovery, development, and research [1].…”

Section: Introductionmentioning

confidence: 99%

Recent Trends of AI in Pharmaceutical Industries: A Review

2024

OAJDA

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Artificial intelligence (AI) algorithms have been used to evaluate actual knowledge, reports on unfavourable occurrences, and publications to monitor post-marketing medication safety and detect possible safety concerns concerning security and respect to standards. AI has also been helpful in signal verification, adverse event prediction, and pharmacovigilance. AI is additionally implemented in streamlining pharmaceutical supply chains, guaranteeing effective distribution, management of inventory, and output. The possibilities of AI in PKPD studies are being recognized by pharmaceutical firms more and more. AI includes significant tools and techniques that help improve the processes involved in medication development and discovery. GNS Medical Care, AstraZeneca, Atom wise, Recursion Pharmaceuticals, and Insilico Medicines are a few instances. AI has been useful in the advancement of strategies for the rapid and perfect manufacture of dosage forms. AI possesses the possibility to completely overhaul the medical industry in the future by driving up the process of medication creation and discovery. With the use of virtual screening techniques, large chemical libraries will be effectively assessed to determine therapeutic candidates with the necessary lead compound identification time. By reviewing genomes, proteomes, and health data, AI-enabled precision nursing may be able to divide up patients, determine the results of treatment, and personalize medications. Using generative models and deep learning, scientists can produce novel molecules with targetbinding properties that increase drug efficacy and decrease side effects. AI will also enable dosage formulations tailored to individual patients. AI algorithms will improve drug formulations as well as delivery systems for improving health service outcomes by accounting for factors unique to each patient, such as size, age, heritage, and medical condition disease. Security evaluations he transform as a result of AI algorithms' ability to predict the toxicity and potential negative effects of medications.

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Investigating the Impact of Machine Learning in Pharmaceutical Industry

Cited by 10 publications

References 30 publications

AI'S Healing Touch: Examining Machine Learning's Transformative Effects On Healthcare

AI'S Healing Touch: Examining Machine Learning's Transformative Effects On Healthcare

Potential Integration Of Machine Learning Algorithm And Manufacturing Execution System In The Lean Six Sigma Method To Improve Operational Excellence At ABC Farma Company

Recent Trends of AI in Pharmaceutical Industries: A Review

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