Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects.
Carlo Giacomo LeoMaria Rosaria TumoloSaverio SabinaRiccardo ColellaVirginia RecchiaGiuseppe PonziniDimitrios Ioannis FotiadisAntonella BodiniPierpaolo MincaronePublished in: International journal of environmental research and public health (2022)
The application of in silico medicine is constantly growing in the prevention, diagnosis, and treatment of diseases. These technologies allow us to support medical decisions and self-management and reduce, refine, and partially replace real studies of medical technologies. In silico medicine may challenge some key principles: transparency and fairness of data usage; data privacy and protection across platforms and systems; data availability and quality; data integration and interoperability; intellectual property; data sharing; equal accessibility for persons and populations. Several social, ethical, and legal issues may consequently arise from its adoption. In this work, we provide an overview of these issues along with some practical suggestions for their assessment from a health technology assessment perspective. We performed a narrative review with a search on MEDLINE/Pubmed, ISI Web of Knowledge, Scopus, and Google Scholar. The following key aspects emerge as general reflections with an impact on the operational level: cultural resistance, level of expertise of users, degree of patient involvement, infrastructural requirements, risks for health, respect of several patients' rights, potential discriminations for access and use of the technology, and intellectual property of innovations. Our analysis shows that several challenges still need to be debated to allow in silico medicine to express all its potential in healthcare processes.
Keyphrases
- healthcare
- electronic health record
- big data
- health information
- mental health
- public health
- molecular docking
- end stage renal disease
- human health
- social media
- risk assessment
- data analysis
- chronic kidney disease
- artificial intelligence
- deep learning
- peritoneal dialysis
- health promotion
- climate change
- patient reported