Considerations for addressing bias in artificial intelligence for health equity.

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these "Considerations" is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.

Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine.

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice; the role of -omics technologies in Health Care; the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases; and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances.

Resistencia del P. falciparum a drogas en Colombia / Drug resistence of P. falciparum in Colombia

1. No se conoce prevalencia real de resistencia a las drogas en Colombia. 2. No se conoce la variacion de la resistencia a drogas entre 1961-1986 en Colombia. 3. El mayor numero de personas estudiadas a resistencia a drogas fue en 1961. 4. Un gran numero de trabajos lo que evaluan es respuesta a drogas y no propiamente resistencia. 5. Se dan cifras de resistencia a drogas en Colombia sin mencionar trabajos que las respaldan. 6. Algunos estudios parten de la premisa de estar trabajando con pacientes cloroquino resistentes y no se cita como se midio esta resistenica. 7. Muchos trabajos dan cifras de resistencia a drogas en Colombia y aun las regiones cloroquino resistentes por informacion personal o "datos aun no publicados". 8. Algunos trabajos evaluaron respuesta a drogas haciendo controles despues del dia 14 sin sacar al paciente de zona endemica. 9. Se nota mejor calidad en los estudios realizados en los dos ultimos anos. No solo por el numero de pacientes, como por la parte tecnica en la aplicacion del concepto de resistencia tanto in vivo como in vitro