Artificial Intelligence (AI) tools have been introduced to all areas including healthcare. AI is expected to have the potential to significantly improve existing technologies, sharpen personalised medicines, and benefit historically underserved populations with an influx of big data.
To harness the power of AI, the healthcare community must ensure that AI tools are trustworthy and they don’t end up perpetuating biases that exist in the current system. U.S. Researchers aim to create an initiative to support AI research in health care and build a robust infrastructure that can aid scientists and clinicians in pursuing this mission.
Thought leaders in academia, industry and government who are working to improve healthcare gathered to discuss the current state of AI adoption healthcare. The discussion includes new machine learning techniques that support fairness, personalisation, and inclusiveness, identifying key areas of impact in healthcare delivery, and discussing regulatory and policy implications.
The U.S. researcher received a grant to create a community platform supporting equitable AI tools in healthcare. The project’s ultimate goal is not to solve an academic question or reach a specific research benchmark, but to actually improve the lives of patients worldwide. The researchers insist that AI tools should not be designed with a single population in mind but instead be crafted to be reiterative and inclusive, to serve any community or subpopulation. The AI tool needs to be studied and validated across many populations, usually in multiple cities and countries.
This call to action is a response to health care in 2020. A U.S. Professor spoke on how health care providers typically prescribe treatments and why these treatments are often incorrect. Traditionally, a doctor collects information on their patient, then uses that information to create a treatment plan. The decisions providers make can improve the quality of patients’ lives or make them live longer, but this does not happen in a vacuum.
A complex web of forces can influence how a patient receives treatment. These forces go from being hyper-specific to universal, ranging from factors unique to an individual patient to bias from a provider, such as knowledge gleaned from flawed clinical trials to broad structural problems, like disproportionate access to care.
As machine learning researchers detect preexisting biases in the health care system, they must also address weaknesses in algorithms themselves. They must grapple with important questions that arise in all stages of development, from the initial framing of what the technology is trying to solve to overseeing deployment in the real world.
The researchers emphasised the importance of creating a large amount of data that is diverse at the same time. They are trying to train algorithms to be robust, private, fair, and high-quality and they require large-scale data sets for research use.
More U.S. organisations start to recognise the power of harnessing diverse data to create more equitable health care. For example, an unprecedented project from the National Institutes of Health aims to bridge the gap for historically under-recognised populations by collecting observational and longitudinal health data on over 1 million Americans. The database is meant to uncover how diseases present across different sub-populations.
Prescribing medications correctly to patients is a challenge for doctors and the traditional method is inadequate, therefore AI is urgently needed in this area. As reported by OpenGov Asia, U.S. Researchers developed a machine learning (ML) to detect medication errors thus help doctors to make better decisions and improve patient safety and quality of care.
Medication errors are a growing financial and healthcare burden that results in economic costs. Medication errors can occur during any stage of the medication process, including prescribing, dispensing, administration, and monitoring, with errors in prescribing accounting for 50% of the total. Hence, ML can provide insights on patterns and predictions to help doctors make data-driven decisions. For technology to assist in solving these problems, ML learning must understand these variables. For this to be successful, data must be properly collected, organised, and maintained.
