Tech Innovations in Healthcare Response to Colleague

RESPONSE 2 (BLOG 1)

Response 2 (Blog 1)

Susan V

Susan V points out that AI, Machine Learning, Genomics, Precision Health, and Robotics improve healthcare outcomes by offering clinical decision support through diagnosis, disease prevention, and treatment. According to Habuza et al. (2021), innovations such as machine learning and AI facilitate, amongst other things, accurate diagnosis by eliminating subjectivity. In addition, there are plenty of enhanced possibilities for improved healthcare services. For instance, following diagnosis, disease patterns can be assessed easily using available data – a move which has the potential to further enhance the health and wellbeing of entire populations (Habuza et al, 2021). Susan indicates that the said applications integrate big data by transforming data to be used in treatment and prevention of disease and providing insights for the future. According to Filipp (2019), there are also opportunities for innovations in tech to be utilized in automated disease classification and development of data-driven insights in biomedicine through multi-omics data.

Blessing E

Blessing E indicates that Artificial Intelligence (AI), machine learning, robots, genomics, and precision health applications improve efficiency and enhance patient outcomes by overcoming the shortcomings of human practitioners and enhancing their efficiency. According to Davenport and Kalakota (2019), there are plenty of opportunities for the utilization of, for instance AI. For instance, in the words of the authors, AI “can be used to diagnose cancer, triage critical findings in medical imaging, flag acute abnormalities, provide radiologists with help in prioritizing life threatening cases, diagnose cardiac arrhythmias, predict stroke outcomes, and help with the management of chronic diseases” (Davenport and Kalakota, 2019, p. 95). Blessing also indicates that the said technologies integrate big data except, robotics given that they offer predictions, accuracy, insights, and perspectives that are not offered by other approaches. It is also important to note that according to Johnson et al. (2021), better prediction helps in assessing risks among patients. The ability to generate better insights from available (as well as incoming) data empowers clinical decision making and enables the system to learn and function in an increasingly meaningful manner (Johnson et al., 2021).

References

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94

Filipp, F. V. (2019). Opportunities for Artificial Intelligence in Advancing Decision Medicine. Current Genetics Medicine Reports, 7, 208-213.