Perspectives in biology and medicine
-
Perspect. Biol. Med. · Jan 2018
The Need for Beneficence and Prudence in Clinical Innovation with Autologous Stem Cells.
The term innovation is frequently used as a justification for allowing clinicians to offer unproven autologous stem cell-based interventions (SCBIs) to their patients. Proponents of this kind of innovation (which we refer to as "clinical innovation") argue that physicians should be free to administer whatever interventions they choose, and informed consumers should be free to receive them. ⋯ We also argue that there is a need for a clearer distinction between the definition of clinical innovation with autologous stem cells, which is morally neutral, and its justification, which entails a commitment to beneficence and prudence. Finally, we argue that regulation of clinical innovation with autologous stem cells needs to be based on a bioethics of innovation that attends to beneficence and prudence alongside other ethical principles.
-
The pace of biomedical innovation is important because it determines the rate of progress in medicine and allied disciplines. A review of the history of medical advances reveals that the three decades from 1950 to 1980 were a particularly innovative time. Subsequent decades have seen fewer seminal advances, despite continued improvements in many biomedical technologies. ⋯ Overall, the pace of innovation may be slower than in the past, especially when measured against the biomedical knowledge base available today versus that available then. Ten factors are discussed that could be slowing biomedical innovation. If biomedical innovation is slowing, then restoring a faster pace would require the cooperation of government, industry and academia, for the problems are multifaceted and intertwined.
-
Perspect. Biol. Med. · Jan 2018
The Tension Between Big Data and Theory in the "Omics" Era of Biomedical Research.
"Big data," a consequence of the "omics" technologies and its analysis by machine learning, have changed the climate of thought in biomedical sciences, shifting the demography of expertise and culminating in a new role: "data scientist." While historically the inquiry on the nature of organisms started with theories (logical reasoning) but no data, we now live in an era of data but no theory. A tacit assumption of modern data analytics is that correlations and clusters in the data constitute knowledge. Through support of technology and data collection, funding agencies promoted this attitude, while neglecting hypothesis-driven inquiry and theory. ⋯ This article provides key concepts for a fruitful discussion, examines the dualism between data and theory, and proposes how they synergize. Data scientists must learn to appreciate theory, but if the most value is to be extracted from data, theorists should not dismiss brute-force empirical pattern recognition in data. The patterns could motivate the erection of new theories, much as Kepler's law represented a formal "summary" of astronomic data on which Newton's laws could be tested.