Question

In May, 2018, Google's Medical Brain team published a paper in Nature announcing a new health care

initiative, an Artificial Intelligence algorithm designed to predict patient outcomes, duration of

hospitalization, even the likelihood of death during hospitalization. A great deal of attention is being

paid to mortality statistics, or the death algorithm, which has been used in two instances. In the first

case, at Hospital A, the algorithm was 95 percent accurate in predicting death; in the second case, at

Hospital B, it was 93 percent accurate. In both of these cases, the AI algorithm performed significantly

better than the more traditional models or techniques of predicting patient outcomes.

Google researchers believe the algorithm will reduce health care cost, increase patient-physician face

time, and reduce the burden of current data systems which rely heavily on cumbersome and labor-

intensive data mining techniques. The AI algorithm is based on very large amounts of anonymous

patient data (one previous algorithm used forty-six billion pieces of data), for which use patients and

hospitals had consented and approved. Proper safeguards or data security, privacy, and various other

HIPPA concerns are a major issue, especially in light of data privacy concerns with companies in the

past such as Facebook.

This technology may also be exciting for health insurance companies. Insurance companies love data

because it allows them to better estimate the cost of covering an individual. The AI algorithm is the

first of its kind due the large amount of data it uses, and promises to become one of the most effective

tools for predicting health care cost and outcomes.

There are, however, many unknowns. How will this new AI affect health insurance and patient

treatment? Will health insurance companies have access to the data? How will accessibility and

affordability of health insurance change if there is reason to believe an individual has increased risk

factors for disease progression, hospitalization, or death? Will physicians still use due diligence for

medical diagnoses or will they simply rely on the AI outcomes? What will happen when the algorithm

and a physician disagree?

What should the proper use of such algorithms be in a hospital setting (Looking at ethics)?

Answer 1

The use of AI is predicted to decrease medical costs as there will be more accuracy in diagnosis and better predictions in the treatment plan as well as more prevention of disease.

Other future uses for AI include Brain-computer Interfaces (BCI) which are predicted to help those with trouble moving, speaking or with a spinal cord injury. The BCIs will use AI to help these patients move and communicate by decoding neural activates.

Artificial intelligence has led to significant improvements in areas of healthcare such as medical imaging, automated clinical decision-making, diagnosis, prognosis, and more. Although AI possesses the capability to revolutionize several fields of medicine, it still has limitations and cannot replace a bedside physician.

Healthcare is a complicated science that is bound by legal, ethical, regulatory, economical, and social constraints. In order to fully implement AI within healthcare, there must be "parallel changes in the global environment, with numerous stakeholders, including citizen and society."

Ethical concerns-

Data collection

In order to effectively train Machine Learning and use AI in healthcare, massive amounts of data must be gathered. Acquiring this data, however, comes at the cost of patient privacy in most cases and is not well received publicly. For example, a survey conducted in the UK estimated that 63% of the population is uncomfortable with sharing their personal data in order to improve artificial intelligence technology.The scarcity of real, accessible patient data is a hindrance that deters the progress of developing and deploying more artificial intelligence in healthcare.

Automation

According to a recent study, AI can replace up to 35% of jobs in the UK within the next 10 to 20 years.However, of these jobs, it was concluded that AI has not eliminated any healthcare jobs so far. Though if AI were to automate healthcare related jobs, the jobs most susceptible to automation would be those dealing with digital information, radiology, and pathology, as opposed to those dealing with doctor to patient interaction.

Automation can provide benefits alongside doctors as well. It is expected that doctors who take advantage of AI in healthcare will provide greater quality healthcare than doctors and medical establishments who do not.AI will likely not completely replace healthcare workers but rather give them more time to attend to their patients. AI may avert healthcare worker burnout and cognitive overload

AI will ultimately help contribute to progression of societal goals which include better communication, improved quality of healthcare, and autonomy.

Bias

Since AI makes decisions solely on the data it receives as input, it is important that this data represents accurate patient demographics. In a hospital setting, patients do not have full knowledge of how predictive algorithms are created or calibrated. Therefore, these medical establishments can unfairly code their algorithms to discriminate against minorities and prioritize profits rather than providing optimal care.

There can also be unintended bias in these algorithms that can exacerbate social and healthcare inequities. Since AI’s decisions are a direct reflection of its input data, the data it receives must have accurate representation of patient demographics. White males are overly represented in medical data sets.Therefore, having minimal patient data on minorities can lead to AI making more accurate predictions for majority populations, leading to unintended worse medical outcomes for minority populations. Collecting data from minority communities can also lead to medical discrimination. For instance, HIV is a prevalent virus among minority communities and HIV status can be used to discriminate against patients.^[ However, these biases are able to be eliminated through careful implementation and a methodical collection of representative data.

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