How AI Is Making Treatments More Personal for Every Patient (original) (raw)
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Ai's Impact on Personalized Medicine: Tailoring Treatments for Improved Health Outcomes
Engineering science & tecnology journal, 2024
This review paper explores the transformative impact of artificial intelligence (AI) on personalized medicine and its potential to revolutionize healthcare outcomes. AI technologies, ranging from data analysis and interpretation to diagnostic tools and treatment planning, offer unprecedented opportunities for tailoring medical interventions to individual patient characteristics. Through sophisticated algorithms, AI facilitates the analysis of complex biological data, predicts disease risks, and enhances diagnostic accuracy. Furthermore, AIpowered personalized medicine promises to expand access to high-quality healthcare and address global health disparities. However, challenges such as data privacy, bias, and regulatory hurdles must be addressed to ensure the responsible integration of AI into healthcare practices. This paper underscores the importance of interdisciplinary collaboration, ethical considerations,
Artificial Intelligence toward Personalized Medicine
Pharmaceutical Sciences and Research, 2021
In current medical practice when a patient feels symptoms he/she would consult the doctor. The doctor then gives medication in a one-fits-all fashion. However, recent genetics studies had shown that different genetic makeup can results in different effects on medication, so the medication should be customed for every individual. The main idea of "personalized medicine" is to provide the right intervention including medication to the right patient at the right time and dose. With this approach, the medication paradigm would shift from curative to preventive. The rise of personalized medicine had been possible because the information from ever-increasing biomolecular (proteomics, genomics, and other omics) and health-related data are successfully "mined" by Artificial Intelligence (AI) tools. In this paper, we proposed that AI systems toward personalized medicine must have acceptable performance, be readily interpretable by the clinical community, and be validated in a large cohort. We examined a few landmark papers with the keyword "AI for personalized medicine application"; 1) automatic image-based patient classification, 2) automatic gene-based cancer classification, and 3) automatic health-record heart failure with preserved ejection fraction patient phenotyping. All the examples are evaluated by their performance, interpretability, and clinical validity. From the analysis, we concluded that AI for personalized medicine could benefit by five factors: (1) standardization and pooling of genetics and health data, nationally and internationally, (2) the use of multi-modalities data, (3) disease specialist to guide the development of AI model, (4) investigation of AI-finding by clinical community, and (5) follow-up of AI-finding by the large clinical trial.
Integrating Genomic Data with AI Algorithms to Optimize Personalized Drug Therapy: A Pilot Study
Library Progress International, 2024
Personalized medicine has become more prominent in the course of the last few years to improve treatment methods by taking into account patients’ genetic makeup. Combining the genomic information into powerful new AI platforms in drug therapies opens up the way of reducing drug toxicity while enhancing the prospects for drug efficacy. This pilot study aims to determine the possibilities of using AI to analyze genomics data to help improve the approachability and effectiveness of drug therapies, which has been a major challenge given the lacunae in precision in the treatment strategies used. This pilot study is intended to enroll 50 patients with diverse chronic diseases. Targeted gene-specific sequencing was performed to obtain polymorphic loci on drug metabolism and treatment efficacy. AI tools such as machine learning models are used to help find patterns and relationships between genomic data and treatment results and risks. These were then compared to clinical outcomes in order to determine the viability of the AI-integrated method for recommending drug regimens. This study shows that the incorporation of genomic data in conjunction with AI greatly improves the accuracy of individualized pharmacotherapy. The AI-generated suggestions matched well with the enhanced patient experience to show the potential of this concept in the real world. It employs a broader clinically ascertained population and is warranted to replicate these findings, supporting the benefits of using genomic-informed AI applications for drug therapy to drive further development of personalized medicine.
Advancing health care via artificial intelligence: From concept to clinic
European Journal of Pharmacology, 2022
Ever Since, pharmaceutical companies are facing challenges to develop new drug products faster and economical with good quality, safety and efficacy. The advent of Artificial intelligence (AI) with computational technology empowers scientists, impacts society at a great scale by developing new drugs at rapid pace. Artificial intelligence is the science and engineering of creating intelligent machines using personified knowledge. There are many opportunities to apply AI tools to the drug discovery pipeline. Examples include target identification, identification of biomarkers, molecular modelling, synthesis of molecules, predicting toxicity and picking up leads. Further, this technology also helps the clinical scientists in clinical trial design, execution and real-time analysis. Altogether it facilitates the process of drug discovery, development and also provides better therapy to the patients. Apart from drug discovery and development, AI also has applications in the area of diagnosis, drug delivery, patient adherence and better monitoring of safety. There are many instances where AI can perform tasks better than humans and aid healthcare providers in treating patients. In this article, we have provided discussion on how AI is advancing the health care field to achieve greater success.
AI-DRIVEN PRECISION MEDICINE: REVOLUTIONIZING PERSONALIZED TREATMENT PLANS
IAEME, 2024
Precision medicine has been described as a revolution currently occurring in the field of healthcare. The advance of Integrated Artificial Intelligence (IAI) in precision medicine is providing a new perspective of the healthcare sector and creating highquality efficient, individualized treatment plans. In this article, the application of artificial intelligence (AI) in precision medicine has been examined with a focus on how it improves the identification of disease risks, treatment management, and patient outcomes based on the analysis of genetics and clinical and environmental information. Applications include AI algorithms for machine learning and deep learning to enhance biomarkers' detection, the production of accurate models, and the discovery of suitable treatments for individuals. In the area of precision medicine, AI is not limited to diagnostic and treatment recommendations, though; it is also employed in the identification of drugs for treatment, management of clinical trials, and tracking of patients' progress. With the help of AI in healthcare, patients can receive customized and innovative care that minimizes the side effects of therapies or treatments, has higher chances of effectiveness, and enhances patient's health overall. Of course, the use of AI in precision medicine has some limitations, such as issues with data privacy and protection, the use of large and diverse datasets (especially in the case of genetic variations), and the likelihood of biases in the use of AI algorithms. Solving these issues is difficult and can only be done through teamwork where clinicians, data scientists, and policymakers can ensure that precision medicine with AI is both good and universally available. Still precision medicine is a promising interdisciplinary approach to treating patients based on their particular features, and this article gives an overview of different technologies, as well as methods and applications of precision medicine in the context of artificial intelligence. It further highlights the advantages and uncertainties concerning this fairly new and progressing area and presents suggestions as to the conception of AI in the healthcare direction.
AI System for Personalized Medicine and Treatment Planning
2024
AI, or Artificial Intelligence, refers to the simulation of human intelligence processes by machines, primarily computers. It involves creating intelligent agents, which are systems that can reason, learn, and act autonomously. Schork N. J. (2019). The future of healthcare lies in personalization, tailoring treatment plans to each individual's unique needs and characteristics. Traditional, one-size-fits-all approaches often miss crucial nuances, leading to suboptimal outcomes. This is where AI (Artificial Intelligence) emerges as a game-changer, offering unprecedented capabilities for personalized medicine and treatment planning. AI System for Personalized Medicine and Treatment Planning dives into this exciting realm, leveraging the power of AI to create a transformative system. This system aims to achieve: Accurate Diagnosis and Prognosis, by analyzing vast datasets of medical records, clinical data, and even genetic information, AI algorithms can identify patterns and relationships that might escape human experts. This can lead to earlier and more precise diagnoses, as well as better prediction of potential health risks. Johnson, K. B.(2021). Tailored Treatment Recommendations, moving beyond standardized protocols, AI can suggest treatment options specifically suited to each patient's unique profile. This can consider factors like age, medical history, genetic makeup, and individual response to previous medications. Enhanced Decision Support for Healthcare Professionals, equipped with AI-powered insights, doctors and other healthcare providers can make more informed decisions, considering evidencebased recommendations alongside their own expertise and patient interaction. Improved Patient Outcomes as patients receive personalized treatment plans that cater to their specific needs, the potential for better clinical outcomes significantly increases. This can translate to faster recovery times, reduced side effects, and improved overall health. However, building such a system comes with challenges. Data privacy, ethical considerations, and regulatory compliance are crucial areas to address. This project, AI System for Personalized Medicine and Treatment Planning takes a responsible approach.
AI in Medical Research Applications & Considerations -
CSA , 2024
Artificial intelligence (AI) has revolutionized medical research by leveraging machine learning (ML), particularly deep learning (DL), to enhance various facets of healthcare. This paper explores the profound impact of AI across several domains within medical research. This study investigates AI's applications in drug discovery, encompassing de novo drug design, retrosynthesis, reaction prediction, and protein engineering. It also delves into AI's role in diagnosis, treatment, and personalized medicine, focusing on the concept of a Cognitive Digital Twin (CDT). This innovative approach holds promise for predicting treatment outcomes, identifying health risks, and enabling proactive interventions. AI's ethical and legal challenges in medical research are also critically examined. Our opinion is that recent advancements in AI, particularly in deep learning, have significantly bolstered these applications. Emerging methodologies in AI are poised to address complex challenges in drug discovery. Moreover, integrating open data sharing and collaborative model development is pivotal for advancing AI-driven drug discovery . The paper also underscores the ethical and 1 legal considerations accompanying AI's rapid integration into medical research, urging prioritization of these critical issues. We conclude that AI continues to evolve with the potential to transform medical research by enhancing deep learning capabilities and fostering interdisciplinary collaboration. AI promises to drive drug discovery, personalized medicine, and healthcare delivery breakthroughs.
A Review on Role of Artificial Intelligence: In Diagnosis of Disease and Drug Management
EPRA international journal of research & development, 2023
Artificial Intelligence (AI) revolutionizes healthcare through advanced algorithms, analyzing patient data for precise disease diagnosis and optimized drug management. Despite challenges in data privacy and ethical deployment, AI's transformative potential holds promise for improved patient care. Collaboration and innovation will drive the future of AI in healthcare, shaping a dynamic landscape.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE: REVOLUTIONIZING DISEASE DIAGNOSIS AND TREATMENT PLANNING
IAEME PUBLICATION, 2024
Artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionizing disease diagnosis and treatment planning. This article explores the growing role of AI in healthcare, focusing on its applications in various medical domains, such as radiology, oncology, and genomics. By leveraging advanced algorithms and machine learning models, AI systems can analyze vast amounts of medical data, identify patterns and anomalies, and assist healthcare professionals in making informed decisions. The article highlights the potential benefits of AI in facilitating early diagnosis, personalized medicine, and improved patient outcomes. It also discusses the advancements in AI technologies, including sophisticated deep learning models, processing and interpreting complex medical data, and integration with other technologies such as Internet of Things (IoT) and blockchain. However, the article also addresses the challenges associated with implementing AI in healthcare, such as ethical concerns, data privacy and security, the need for extensive and unbiased datasets, and the establishment of robust regulatory frameworks. The future outlook for AI in healthcare is promising, with ongoing advancements and growing interest from stakeholders. Nonetheless, the success of AI in healthcare relies on addressing the challenges, ensuring transparency and accountability, and maintaining a humancentered approach. By harnessing the power of AI responsibly and effectively, healthcare systems can pave the way for improved patient care and outcomes.
Cognizance Journal of Multidisciplinary Studies (CJMS), 2024
As noted, healthcare delivery systems are systems that always present organizational difficulties for any user and everyone related to these systems, but AI today is one of the widely-discussed and most frequently considered innovations that have prospects for radical improvement of the patient’s state and quality of life. AI solutions are on the verge of becoming integrated into clinical practice thanks to enhanced developments in the field, which means that there is a need to cover the part played by these technologies in healthcare and equip healthcare providers with the knowledge and resources needed to achieve that. This review article provides a broad, although dated, systematic synthesis of AI in clinical practice today, reviewing potential uses of AI in disease taxonomy and diagnosis, treatment suggestions, patient participation, and utilizing methodologies to consider the ethical and legal issues of AI and the persistent need for human insights and expertise. In order to establish the nature of the relationships and possible effects of AI in healthcare environments, the paper reviews the literature using PubMed/Medline, Scopus, and EMBASE databases. According to the findings, the application of AI technologies in health care delivery can greatly improve disease diagnosis, therapy choice, and clinical testing because, in contrast to human decision-making, AI algorithms analyze extensive data sets and outcompete human experts in specific tasks. It is accurate, cost-effective, time efficient, and free from human errors, advances the fields of pharmacogenomics and personalized medicine, optimizes the dosing of drugs, improves overall population health, establishes virtual healthcare services, and enhances mental health care, education, and trust between patients and physicians. Nevertheless, these and the following issues cannot be left unresolved: data privacy, AI bias, and the indispensability of the human factor.