%0 Journal Article %@ 2563-3570 %I JMIR Publications %V 6 %N %P e69800 %T Harnessing AI and Quantum Computing for Revolutionizing Drug Discovery and Approval Processes: Case Example for Collagen Toxicity %A Braga,David Melvin %A Rawal,Bharat %K generative AI %K quantum computing %K computational data %K new drug discovery %K computer-aided drug discovery %K artificial intelligence %D 2025 %7 22.7.2025 %9 %J JMIR Bioinform Biotech %G English %X Artificial intelligence (AI) and quantum computing will change the course of new drug discovery and approval. By generating computational data, predicting the efficacy of pharmaceuticals, and assessing their safety, AI and quantum computing can accelerate and optimize the process of identifying potential drug candidates. In this viewpoint, we demonstrate how computational models obtained from digital computers, AI, and quantum computing can reduce the number of laboratory and animal experiments; thus, computer-aided drug development can help to provide safe and effective combinations while minimizing the costs and time in drug development. To support this argument, 83 academic publications were reviewed, pharmaceutical manufacturers were interviewed, and AI was used to run computational data for determining the toxicity of collagen as a case example. The research evidence to date has mainly focused on the ability to create computational in silico data for comparison to actual laboratory data and the use of these data to discover or approve newly discovered drugs. In this context, “in silico” describes scientific studies performed using computer algorithms, simulations, or digital models to analyze biological, chemical, or physical processes without the need for laboratory (in vitro) or live (in vivo) experiments. Digital computers, AI, and quantum computing offer unique capabilities to tackle complex problems in drug discovery, which is a critical challenge in pharmaceutical research. Regulatory agents will need to adapt to these new technologies. Regulatory processes may become more streamlined, using adaptive clinical trials, accelerating pathways, and better integrating digital data to reduce the time and cost of bringing new drugs to market. Computational data methods could be used to reduce the cost and time involved in experimental drug discovery, allowing researchers to simulate biological interactions and screen large compound libraries more efficiently. Creating in silico data for drug discovery involves several stages, each using specific methods such as simulations, synthetic data generation, data augmentation, and tools to generate, collect, and affect human interaction to identify and develop new drugs. %R 10.2196/69800 %U https://bioinform.jmir.org/2025/1/e69800 %U https://doi.org/10.2196/69800 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e64406 %T Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models %A Chow,James C L %A Li,Kay %+ Princess Margaret Cancer Centre, University Health Network, 7/F, Rm 7-606, 700 University Ave, Toronto, ON, M5G 1X6, Canada, 1 4169464501, james.chow@uhn.ca %K artificial intelligence %K humanistic AI %K ethical AI %K human-centered AI %K machine learning %K large language models %K natural language processing %K oncology chatbot %K transformer-based model %K ChatGPT %K health care %D 2024 %7 6.11.2024 %9 Viewpoint %J JMIR Bioinform Biotech %G English %X The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably. %M 39321336 %R 10.2196/64406 %U https://bioinform.jmir.org/2024/1/e64406 %U https://doi.org/10.2196/64406 %U http://www.ncbi.nlm.nih.gov/pubmed/39321336 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e55632 %T It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine %A Robertson,Alan J %A Mallett,Andrew J %A Stark,Zornitza %A Sullivan,Clair %+ Queensland Digital Health Centre, University of Queensland, Health Sciences Building, Herston Campus, Royal Brisbane and Women's Hospital, Brisbane, 4029, Australia, 61 733465343, c.sullivan1@uq.edu.au %K genomics %K digital health %K genetics %K precision medicine %K genomic %K genomic data %K electronic health records %K DNA %K supports %K decision-making %K timeliness %K diagnosis %K risk reduction %K electronic medical records %D 2024 %7 13.6.2024 %9 Viewpoint %J JMIR Bioinform Biotech %G English %X Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine. %M 38935958 %R 10.2196/55632 %U https://bioinform.jmir.org/2024/1/e55632 %U https://doi.org/10.2196/55632 %U http://www.ncbi.nlm.nih.gov/pubmed/38935958 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 2 %N 1 %P e29905 %T Nonfungible Tokens as a Blockchain Solution to Ethical Challenges for the Secondary Use of Biospecimens: Viewpoint %A Gross,Marielle S %A Hood,Amelia J %A Miller Jr,Robert C %+ Department of Obstetrics, Gynecology and Reproductive Services, University of Pittsburgh Medical Center, 300 Halket Street, Pittsburgh, PA, 15213, United States, 1 412 641 1000, grossms@upmc.edu %K blockchain %K biospecimens %K research ethics %K nonfungible tokens %K research ethics %K health platforms %K HeLa cells %K patient data %K deidentification %K eHealth %K data security %K integrity %D 2021 %7 22.10.2021 %9 Viewpoint %J JMIR Bioinform Biotech %G English %X Henrietta Lacks’ deidentified tissue became HeLa cells (the paradigmatic learning health platform). In this article, we discuss separating research on Ms Lacks’ tissue from obligations to promote respect, beneficence, and justice for her as a patient. This case illuminates ethical challenges for the secondary use of biospecimens, which persist in contemporary learning health systems. Deidentification and broad consent seek to maximize the benefits of learning from care by minimizing burdens on patients, but these strategies are insufficient for privacy, transparency, and engagement. The resulting supply chain for human cellular and tissue–based products may therefore recapitulate the harms experienced by the Lacks family. We introduce the potential for blockchain technology to build unprecedented transparency, engagement, and accountability into learning health system architecture without requiring deidentification. The ability of nonfungible tokens to maintain the provenance of inherently unique digital assets may optimize utility, value, and respect for patients who contribute tissue and other clinical data for research. We consider the potential benefits and survey major technical, ethical, socioeconomic, and legal challenges for the successful implementation of the proposed solutions. The potential for nonfungible tokens to promote efficiency, effectiveness, and justice in learning health systems demands further exploration. %R 10.2196/29905 %U https://bioinform.jmir.org/2021/1/e29905 %U https://doi.org/10.2196/29905