“AI promises to expertly diagnose disease in medical images and scans. However, a close look…suggests these powerful new tools may perpetuate health inequalities.” How can we train algorithms to work for all patients regardless of origin? Many tools are currently trained in richer countries such as the United States, but are expected to recognize patterns in patients all across the world. This difference leads to certain AI interventions only being effective in countries with greater resources.

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Can AI help doctors differentiate between COVID-19 and the regular flu? What information would the technology need and where should we draw those boundaries? Some models are showing promise by sifting through common health data.

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“In health care, a new force is redrawing barriers: algorithms that blindly soak up and perpetuate historical imbalances in access to medical resources.” Investigations reveal algorithms commonly inject racial bias into decisions for vulnerable areas. How can we better redesign software to reduce the infusion of racism into health care as we move towards a more digital age?

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“Digital health companies are making broad promises about immediacy, universal access, affordability, early detection and intervention, treating the untreated, overcoming the social determinants that have been barriers to good health.” Moving forward, it is essential to argue for adherence to standards that ensure the interests of the patient population and balancing these interests with the personal interests of the company.

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Could speech technology help COVID-19 diagnosis? Experts argue that these speech samples can be used without explicit consent or to obtain personal health information, and potentially discriminate against their customers. Although the speech technology could provide some insight into further knowledge of true infection rates, concerns over privacy and the merit of information provided are a small snapshot of the factors to consider with wider implementation of this software.

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How will AI change the foundation of healthcare? What considerations do we have to include for ethics of such technology and necessary training of medical providers? What does it mean? Tom Simonite writes on AI integration into provider hands. Often, AI response can challenge the intuition and the conclusions made by the doctor in practice. These differences leads to trust as a major barrier to the inclusion of more AI into healthcare.

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“Data encompasses problems that can occur when machine learning is trained on bias…AI applications in healthcare often involve robots working with humans.” AI can be trained on information that may further propagate discrimination on the basis of race and machine learning. Where does the blame fall when these applications do more harm than good? Can AI effectively be trained in a way that does not introduce more disparities in healthcare?

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AI could revolutionize all aspects of healthcare, but what are the implications? Experts have come together to compile standards for clinical trials that include AI. Can these standards will prevent harm and increase quality of research?

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Nearly two years after the birth of the first “CRISPR babies” stunned the world, an international group of experts on Thursday warned such human experimentation — in which the DNA of embryos is edited before starting pregnancies — should not be conducted because of unresolved scientific and ethical issues. But the group’s eagerly awaited report detailed the steps that scientists should go through before attempting to create gene-edited babies should countries ever greenlight the procedure.

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Some government agencies that use the software said they were surprised that Google may pick up the locations of certain app users. Others said they had unsuccessfully pushed Google to make a change.

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