Artificial Intelligence and Machine Learning in Healthcare Sector

Chavda, Vivek P.; Patel, Kaushika; Patel, Sachin; Apostolopoulos, Vasso

doi:10.1002/9781119865728.ch13

Cited by 8 publications

(9 citation statements)

References 101 publications

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“…This research emphasized that the fear of technological unemployment should be considered within a broader context. AI's potential extends beyond displacement, offering avenues for professionals to engage in novel tasks that contribute to the development, management, and supervision of AI systems in healthcare ( Chavda et al, 2023 ; Rajpurkar et al, 2022 ). Recognizing this transformative potential is essential for understanding the evolving landscape of healthcare delivery amid AI integration.…”

Section: Discussionmentioning

confidence: 99%

“I Wonder if my Years of Training and Expertise Will be Devalued by Machines”: Concerns About the Replacement of Medical Professionals by Artificial Intelligence

Rony,

Parvin,

Wahiduzzaman

et al. 2024

SAGE Open Nursing

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Background The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown. Objective This qualitative research aimed to investigate healthcare workers’ concerns about artificial intelligence replacing medical professionals. Methods A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis. Results The analysis revealed seven key themes reflecting healthcare workers’ concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient–provider relationships; healthcare policy and regulation. Conclusions This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient–provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.

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Section: Discussionmentioning

confidence: 99%

“I Wonder if my Years of Training and Expertise Will be Devalued by Machines”: Concerns About the Replacement of Medical Professionals by Artificial Intelligence

Rony,

Parvin,

Wahiduzzaman

et al. 2024

SAGE Open Nursing

View full text Add to dashboard Cite

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“…By harnessing AI’s capabilities in data analysis, pattern recognition, and optimization, nanomedicine researchers can accelerate the development of novel nanoscale interventions, improve diagnostics, enhance drug delivery, and advance personalized medicine. AI in nanomedicine holds great potential for revolutionizing healthcare by enabling precise and targeted therapeutic approaches at the nanoscale [ 126 ]. Nanoparticles are used for targeted drug delivery, imaging, and sensing.…”

Section: Ai For Drug Deliverymentioning

confidence: 99%

“…These companies are leveraging AI to analyze large datasets, predict drug–target interactions, optimize drug candidates, and simulate drug responses in biological systems. Some examples include GNS Healthcare [ 233 ], AstraZeneca [ 234 ], Atomwise [ 235 ], Recursion Pharmaceuticals, and Insilico Medicines [ 236 ]. AI has helped to improvise strategies for rapid and more accurate dosage form development.…”

Section: Current Trend: Fairy Tale To the Holy Grailmentioning

confidence: 99%

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora,

Gholap,

Jetha

et al. 2023

Pharmaceutics

Self Cite

162

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Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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“…The constraints of gene size and vector capacity impede the creation of multivalent vaccines. To address these limitations, Epitope-based and polytop/chimeric vaccines, combining epitopes for B lymphocytes and MHC receptors, from various proteins, aim to optimize immune responses, often using adjuvants for improved effectiveness 21 . In vaccine design, understanding the pathogen-host interaction is crucial and Pathogens manipulate the immune response, so it's vital to determine the effective immune response type.…”

Section: Investigation Of Immunomodulatory Activity Of Pav3 Vaccine B...mentioning

confidence: 99%

Production of recombinant chimeric protein of Pseudomonas aeruginosa virulence factors and assessment of its immunogenicity in mice

Aminnezhad,

Abdi-Alia,

Ghazanfari

et al. 2023

Preprint

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Escalating mortality rates attributed to drug-resistant Pseudomonas aeruginosa and the substantial complications arising from its infections present a global health crisis. Consequently, the need for innovative therapeutic approaches, notably immunogenic vaccines, has become imperative. Despite extensive research, it is still uncertain whether Pseudomonas aeruginosa immunization can effectively protect against acute and chronic infections. Additionally, the immune response triggered by this immunization is not strong enough against these bacteria. This study aims to devise a chimeric vaccine encompassing key virulence factors of P. aeruginosa that play roles in distinct infection phases. Moreover, these factors possess the capacity to interact with immune receptors, thereby enhancing innate and acquired immune reactions against this bacterium. In this investigation, an in-depth analysis of the P. aeruginosa profile informs the design of the antigenic chimeric structure named PAV3. This construct incorporates lectin, HIV TAT peptide, the N-terminal fragment of exotoxin A, and the Epi8 outer membrane protein F (OprF). Employing an immunoinformatic approach, B cell epitopes, HTL epitopes, CTL epitopes, and IFN-γ are predicted. The PAV3 construct is then expressed in E. coli strain BL21 and purified using affinity chromatography. Immunogenicity assessment of PAV3 entails measuring total IgG, IgG2a, and IgG1, along with specific cytokines (IFN-γ and IL-4) associated with Th1 and Th2 responses, respectively, in vaccinated BALB/c mice. Vaccination with the chimeric vaccine in BALB/c mice triggers a robust Th1 response characterized by elevated production of IgG2a and IFN-γ. This bodes well for inducing potent humoral and cellular immune responses, affording heightened protection. However, the introduction of alum adjuvant alongside PAV3 redirects the immune response toward Th2, leading to reduced protection against internal P. aeruginosa challenges compared to PAV3 alone. In summation, the designed vaccine elicits innate, humoral, and cellular immune responses, offering a promising strategy to manage host-P. aeruginosa interactions and surmount challenges associated with P. aeruginosa vaccination.

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Artificial Intelligence and Machine Learning in Healthcare Sector

Cited by 8 publications

References 101 publications

“I Wonder if my Years of Training and Expertise Will be Devalued by Machines”: Concerns About the Replacement of Medical Professionals by Artificial Intelligence

“I Wonder if my Years of Training and Expertise Will be Devalued by Machines”: Concerns About the Replacement of Medical Professionals by Artificial Intelligence

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Production of recombinant chimeric protein of Pseudomonas aeruginosa virulence factors and assessment of its immunogenicity in mice

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