AI Healthcare Journey

Younger Adult (18-65)

Younger Adult

(18-65)

Advantages

Younger adults are more familiar with recent technologies and may be more open to using AI-based healthcare services compared to older adults

AI healthcare services may be more accessible to younger adults who are more likely to live in urban areas with greater access to technology and healthcare resources

Barriers

Younger adults may have less experience with healthcare services overall and may not know how to navigate AI-based healthcare systems effectively

Some AI-based healthcare services may require expensive technology, such as smartphones or wearable devices, which may not be accessible to all younger adults

Younger adults may have limited access to healthcare resources due to other barriers (location/finances), which may also limit their access to AI-based healthcare services

Older Adult

Older Adult

>65

Advantages

Older adults are more likely to have chronic health conditions and AI can help rwith the diagnosis and management of these conditions

AI can provide personalized treatment plans based on an older adult's specific health needs and medical history

AI helps with medication management, ensuring that older adults take the correct dosage of medication at the right time

AI can help with monitoring and detecting changes in an older adult's health, allowing for early intervention and prevention of health complications

Barriers

Limited access to technology or may not be comfortable using it, making it difficult to access AI healthcare services

There may be a lack of AI healthcare services specifically designed for older adults, as many AI applications are currently focused on younger populations or specific health conditions

Older adults may have increased concerns about privacy and data security, particularly when it comes to sharing personal health information with AI systems

POC

Due to systematic racism limited access to technology and internet services, which may make it more difficult to access AI healthcare tools

Lower trust in the healthcare system which translates into being reluctant to use AI healthcare services (Iatrophobia)

Higher rates of underlying health conditions make AI healthcare more advantageous

Less financial resources and worse health insurance, which may make AI healthcare less affordable for them.

Also certain technologies may not be trained on data sets that represent POC, which may harm the effectivenss of AI in informing a diagnosis.

White

Due to systematic racism there is often greater access to technology and internet services, which are often necessary for accessing AI healthcare tools

More trust in the healthcare system and therefore willingness to use AI healthcare services

More financial resources and better health insurance may make AI healthcare more affordable for them

Upper Class

Have the financial resources to access advanced healthcare services, including those utilizing AI technology

More flexibility in their day-to-day lives to seek out and utilize AI healthcare options

Higher levels of health literacy, make it easier to understand and utilize AI healthcare options effectively

Middle Class

More financial resources than low-income individuals, but still face financial barriers to accessing advanced healthcare services

Live in areas with limited access to advanced healthcare facilities

Limited time to seek out and utilize AI healthcare options due to work or family commitments

Varying levels of health literacy, depending on education and other factors

Lower Class

Lack of financial resources to access healthcare services, including those utilizing AI technology

Live in areas without access to advanced healthcare facilities, limiting their ability to utilize AI healthcare options

Lack of access to the technology required to access AI healthcare options, such as a smartphone or computer

Lower levels of health literacy, make it difficult to understand and utilize AI healthcare options effectively

Rural

AI has the potential to improve healthcare accessibility for rural communities by enabling remote monitoring, telemedicine, predictive analytics, electronic health records, and medical imaging. However, it is important to ensure that these technologies are developed and implemented in a way that is equitable and accessible to all patients, regardless of their location or socioeconomic status.

Challenges:

areas with better internet connectivity and more digital resources, there is still a digital divide that may limit access to AI-powered healthcare for certain rural residents. This can be due to lack of access to digital devices or limited digital literacy.

While it seems urban populations get the best access to healthcare, there are some issues:

AI algorithms are only as good as the data they are trained on. If the data used to train an AI algorithm is biased, the algorithm can perpetuate that bias. In urban areas with diverse populations, this can be a particular challenge. AI algorithms require large amounts of high-quality data to function effectively. However, in urban areas with diverse populations, data quality can be a challenge. For example, some data may be incomplete, outdated, or biased, which can limit the effectiveness of AI algorithms. Despite living in urban areas with more healthcare facilities, not all urban residents have equal access to healthcare. Some residents may not have health insurance or the financial means to access healthcare services. This can limit their ability to benefit from AI-powered healthcare.

Urban

Suburban

AI has the potential to improve healthcare accessibility for suburban communities as well by providing virtual consultations, personalized medicine, improved diagnostics, predictive analytics, and health monitoring.

Bias in data: AI algorithms are trained on large datasets, and if the data is biased or incomplete, the algorithms can be biased as well. This is especially concerning for females as they have historically been underrepresented in medical research studies, leading to a lack of data on female-specific health issues. This can result in inaccurate or incomplete diagnoses and treatments.

Lack of privacy: Females may be hesitant to share sensitive health information with AI-powered healthcare systems due to concerns about privacy and data security. This can limit the effectiveness of AI-powered healthcare for females.

Female

Male

Since most datatsets are trained on research trials done on men , these alrogithms best fit diagnostics for cis -male patients. The reason alot of trails only study men is to cancel out "fluctuation or hormones" as a confounding variable, however this ends up being harmful to non-male patients.

Other

Limited data: The transgender community has historically been underrepresented in medical research, resulting in limited data on the health needs of this population. This can limit the effectiveness of AI-powered healthcare for transgender individuals, as AI algorithms rely on large, diverse datasets to function effectively.

Bias in algorithms: AI algorithms can perpetuate gender biases if they are trained on biased data. For transgender individuals, this can result in inaccurate diagnoses and treatments that do not address their specific health needs.

Cultural competency: Healthcare providers who use AI-powered healthcare systems may not have the necessary cultural competency to provide effective care for transgender individuals. This can lead to discomfort or mistrust among transgender patients, which can limit their willingness to access AI-powered healthcare.