Trustworthy AI Guidelines in Biomedical Decision-Making Applications: A Scoping Review DOI Creative Commons
Marçal Mora‐Cantallops, Elena García‐Barriocanal, Miguel‐Ángel Sicilia

и другие.

Big Data and Cognitive Computing, Год журнала: 2024, Номер 8(7), С. 73 - 73

Опубликована: Июль 1, 2024

Recently proposed legal frameworks for Artificial Intelligence (AI) depart from some of concepts regarding ethical and trustworthy AI that provide the technical grounding safety risk. This is especially important in high-risk applications, such as those involved decision-making support systems biomedical domain. Frameworks span diverse requirements, including human agency oversight, robustness safety, privacy data governance, transparency, fairness, societal environmental impact. Researchers practitioners who aim to transition experimental models software market medical devices or use them actual practice face challenge deploying processes, best practices, controls are conducive complying with requirements. While checklists general guidelines have been aim, a gap exists between practices. paper reports first scoping review on topic specific domain attempts consolidate existing practices they appear academic literature subject.

Язык: Английский

AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability DOI Creative Commons
Ajay Vikram Singh,

Preeti Bhardwaj,

Peter Laux

и другие.

Frontiers in Toxicology, Год журнала: 2024, Номер 6

Опубликована: Ноя. 26, 2024

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards risks associated with chemical exposures. In recent years, convergence of artificial intelligence (AI), machine learning (ML), omics technologies has revolutionized field assessment, offering new insights into toxicity mechanisms, predictive modeling, management strategies. This perspective review explores synergistic AI/ML deciphering clastogen-induced genomic instability for carcinogenic prediction. We provide an overview key findings, challenges, opportunities integrating highlighting successful applications case studies across diverse sectors. From predicting genotoxicity mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer comprehensive framework understanding exposures mitigating risks. Future perspectives advancing cancer prevention through data integration, advanced techniques, translational research, policy implementation are discussed. By implementing capabilities technologies, researchers policymakers can enhance protection, inform regulatory decisions, promote sustainable development healthier future.

Язык: Английский

Процитировано

11

Voices of Nanomedicine: Blueprint Guidelines for Collaboration in Addressing Global Unmet Medical Needs DOI
Rajendra Prasad, Arnab Ghosh, Vinay Patel

и другие.

ACS Nano, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

The "Voices" under this Perspective underline the importance of interdisciplinary collaboration and partnerships across several disciplines, such as medical science technology, medicine, bioengineering, computational approaches, in bridging gap between research, manufacturing, clinical applications. Effective communication is key to team gaps, enhancing trust, resolving conflicts, thereby fostering teamwork individual growth toward shared goals. Drawing from success COVID-19 vaccine development, we advocate application similar collaborative models other complex health areas nanomedicine biomedical engineering. role digital technology big data healthcare innovation highlighted along with necessity for specialized education practices. This approach decisive advancing solutions, leading improved treatment patient outcomes.

Язык: Английский

Процитировано

1

Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist DOI
Paul Hofman, Iordanis Ourailidis, Eva Romanovsky

и другие.

Lung Cancer, Год журнала: 2025, Номер 200, С. 108110 - 108110

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

0

Challenges for Ethics Review Committees in Regulating Medical Artificial Intelligence Research DOI

Alireza Esmaili,

Amirhossein Rahmani,

Abolhasan Alijanpour

и другие.

Indian Journal of Surgical Oncology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Язык: Английский

Процитировано

0

Trustworthy AI Guidelines in Biomedical Decision-Making Applications: A Scoping Review DOI Creative Commons
Marçal Mora‐Cantallops, Elena García‐Barriocanal, Miguel‐Ángel Sicilia

и другие.

Big Data and Cognitive Computing, Год журнала: 2024, Номер 8(7), С. 73 - 73

Опубликована: Июль 1, 2024

Recently proposed legal frameworks for Artificial Intelligence (AI) depart from some of concepts regarding ethical and trustworthy AI that provide the technical grounding safety risk. This is especially important in high-risk applications, such as those involved decision-making support systems biomedical domain. Frameworks span diverse requirements, including human agency oversight, robustness safety, privacy data governance, transparency, fairness, societal environmental impact. Researchers practitioners who aim to transition experimental models software market medical devices or use them actual practice face challenge deploying processes, best practices, controls are conducive complying with requirements. While checklists general guidelines have been aim, a gap exists between practices. paper reports first scoping review on topic specific domain attempts consolidate existing practices they appear academic literature subject.

Язык: Английский

Процитировано

2