Read more: Evidation, USC and RAND awarded NIH R01 Research Project Grant to develop a representative digital health dataset and novel data science methods

Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.

Further reading

Aug 08 2019

Chan R, Jankovic F, Marinsek N, Foschini L, Kourtis L, Signorini A, Pugh M, Shen J, Yaari R, Maljkovic V, Sunga M, Hee Song H, Joon Jung H, Tseng B, Trister A

Source: KDD 2019

Jul 28 2020

Izmailova ES, Wagner JA, Ammour N, Amondikar N, Bell-Vlasov A, Berman S, Bloomfield D, Brady LS, Cai X, Calle RA, Campbell M, Cerreta F, Clay I, Foschini L, Furlong P, Goldel R, Goldsack JS, Groenen P, Folarin A, Heemskerk J, Honig P, Hotopf M, Kamphaus T, Karlin DR, Leptak C, Liu Q, Manji H, Mather RJ, Menetski JP, Narayan VA, Papadopoulos E, Patel B, Patrick-Lake B, Podichetty JT, Pratap A, Servais L, Stephenson D, Tenaerts P, Tromberg B, Usdin S, Vasudevan S, Zipunnikov V, Hoffmann SC

Source: Clinical and Translational Science