A Reminder That Advanced Technology Needs To Be Spread As Equitably As Possible.
This appeared a few days ago.
Artificial intelligence for global health
Science 22 Nov 2019:
Vol. 366, Issue 6468, pp. 955-956
DOI: 10.1126/science.aay5189
Vol. 366, Issue 6468, pp. 955-956
DOI: 10.1126/science.aay5189
Artificial intelligence (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning, a subset of machine learning based on artificial neural networks, has enabled applications with performance levels approaching those of trained professionals in tasks including the interpretation of medical images and discovery of drug compounds (1). Not surprisingly, most AI developments in health care cater to the needs of high-income countries (HICs), where the majority of research is conducted. Conversely, little is discussed about what AI can bring to medical practice in low- and middle-income countries (LMICs), where workforce shortages and limited resources constrain the access to and quality of care. AI could play an important role in addressing global health care inequities at the individual patient, health system, and population levels. However, challenges in developing and implementing AI applications must be addressed ahead of widespread adoption and measurable impact.
Health conditions in LMICs and HICs are rapidly converging, as indicated by the recent shift of the global disease burden from infectious diseases to chronic noncommunicable diseases (NCDs, including cancer, cardiovascular disease, and diabetes) (2). Both contexts also face similar challenges, such as physician burnout due to work-related stress (3), inefficiencies in clinical workflows, inaccuracies in diagnostic tests, and increases in hospital-acquired infections. Despite these similarities, more basic needs remain unmet in LMICs, including health care workforce shortages, particularly specialist medical professionals such as surgical oncologists and cardiac care nurses. Patients often face limited access to drugs, diagnostic imaging hardware (ultrasound, x-ray), and surgical infrastructures (operating theaters, devices, anesthesia). When equipment is available, LMICs often lack the technical expertise needed to operate, maintain, and repair it. As a result, 40% of medical equipment in LMICs is out of service (4). Conditions are exacerbated in fields that require both specialized workforce and equipment. For example, delivering radiotherapy requires a team of radiation oncologists, medical physicists, dosimetrists, and radiation therapists—together with sophisticated particle accelerator equipment. Consequently, 50 to 90% of cancer patients requiring radiotherapy in LMICs lack access to this relatively affordable and effective treatment modality (5).
LMICs have undertaken substantial health care spending, saving millions of lives by improving access to clean water, vaccinations, and HIV treatments. However, changes in health care needs owing to increased mortality from complex NCDs require high-quality, longitudinal, and integrated care (6). These emerging challenges have been central to the United Nations' Sustainable Development Goals, including the aim to reduce by one-third premature mortality from NCDs by 2030. AI has the potential to fuel and sustain efforts toward these ambitious goals.
Health care–related AI interventions in LMICs can be broadly divided into three application areas (see the figure). The first includes AI-powered low-cost tools running on smartphones or portable instruments. These mainly address common diseases and are operated by nonspecialist community health workers (CHWs) in off-site locations, including local centers and households. CHWs may use AI recommendations to triage patients and identify those requiring close follow-up. Applications include diagnosing skin cancer from photographic images and analyzing peripheral blood samples to diagnose malaria (7); more are expected given the emergence of pocket diagnostic hardware, including ultrasound probes and microscopes. With increasing smartphone penetration, patient-facing AI applications may guide lifestyle and nutrition, allow symptom self-assessment, and provide advice during pregnancy or recovery periods—ultimately allowing patients to take control of their health and reducing the burden on limited health systems.
There is vastly more here with references.
As I read this I thought to myself just how easy it is to be seduced by the glam and glitter of rich country first world technology while forgetting just how much good is possible as such advanced stuff flows down the chain to the less rich and privileged. The rate of penetration and use can be, and is, accelerating. With the cloud and smartphones it is possible to reach billions of people now who may have been left out just a decade ago from such innovation and help.
The article is really worth a read to show just how many areas can be improved.
David.
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