AI in Healthcare: Promising future in need of improved diversity

AI and healthcare

Artificial intelligence (AI) has the potential to revolutionize the healthcare industry, from improving patient outcomes to streamlining administrative tasks. However, issues with public trust persist, as does the need for datasets and highly skilled healthcare data scientists that represent all of society.

Artificial intelligence appears to be a constant source of news. Developments stoke fear as well as inspire hope for the future. That is absolutely the case with AI and healthcare. While the future of AI-driven healthcare offers several benefits, public trust must still be improved, just like the diversity of data scientists and the datasets they use for AI research.

AI in Healthcare

AI can be defined as the ability of machines to perform tasks that would normally require human intelligence. In healthcare, AI is being used to help diagnose diseases, develop treatment plans, and even predict patient outcomes. One of the primary benefits of AI in healthcare is the ability to process vast amounts of patient data quickly and accurately. This can lead to more personalized care, earlier diagnosis of diseases, and more efficient treatment plans.

One example of AI in healthcare is the use of natural language processing (NLP) to extract information from patient records. NLP can analyze unstructured data, such as free-text notes and physician dictations, to identify key information that can aid in diagnosis and treatment. Additionally, AI can be used to monitor patient health in real-time and alert healthcare providers to potential issues before they become serious.

AI can also lead to better ways to help doctors with administrative tasks like the dreaded paperwork physicians face each day. The hope is that offloading those tasks to AI-enabled solutions will free doctors to spend more time on patient care. For instance, Geoff Brumfiel, writing for NPR, says that the startup company Glass Health is hoping to use artificial intelligence chatbots to offer services that could “dramatically reduce the paperwork burden physicians face in their daily lives, and dramatically improve the patient-doctor relationship.”

In addition to this administrative support, Bill Gates extols the potential benefits of AI as a way to reduce the death rate for young children, especially in poor countries. In fact, the Gates Foundation has made it a priority to ensure that AI advancements are used to help the poorest people in the world.

Concerns with public trust and data diversity

Despite all of the benefits, there are several concerns with the development of AI in healthcare. First of all, there is a lack of public trust in the use of AI. An early 2023 survey by the Pew Research Center finds that 60 percent of Americans “say they would feel uncomfortable if their own health care provider relied on artificial intelligence to do things like diagnose disease and recommend treatments …” The same study found that only 38 percent of U.S. adults think AI will lead to better health outcomes for patients.

Artificial intelligence and healthcare, public trust

The concerns with losing the human element in healthcare are understandable, but the issues actually run much deeper. AI-based methods like machine learning and deep learning algorithms are developed and trained on massive healthcare data. But if those algorithms use data that do not accurately represent an entire population, they can negatively impact underrepresented groups.

Kaushal and Altman write in Scientific American that:

“… algorithms trained with gender-imbalanced data do worse at reading chest x-rays for an underrepresented gender, and researchers are already concerned that skin-cancer detection algorithms, many of which are trained primarily on light-skinned individuals, do worse at detecting skin cancer affecting darker skin.”

Furthermore, there are concerns that AI researchers do not reflect the population at large. Carissa Wong reports in Nature that “a lack of racial and gender diversity could be hindering the efforts of researchers working to improve the fairness of artificial intelligence (AI) tools in health care, such as those designed to detect disease from blood samples or imaging data.”

An analysis of 375 research and review articles found significant gaps in ethnic and gender diversity among authors of published research and review articles on the fairness of artificial intelligence in healthcare.

Artificial Intelligence and healthcare gaps in representation

Data diversity at Meharry

Issues of data diversity are paramount at Meharry Medical College, one of the nation’s oldest and largest historically black academic health science centers. The College’s newly formed Enterprise Data and Analytics Division uses data from the Meharry Health System to develop a diverse data ecosystem. The data includes a significant amount of records from underrepresented groups and is essential to creating a self-reliant and modern ecosystem that will support research applications.

“The goal is to make Meharry self-dependent and capable of integrating data from multiple and disparate sources such as electronic health records (EHR), in-silico biology, genomic technologies, medical devices, biosensors, social and environmental exposure, and financial data into Meharry’s own modern data science ecosystem,” says Ashutosh Singhal, Ph.D., chief data officer and associate vice president, clinical data stewardship.

These comprehensive datasets, powered by Meharry’s high-performance computing facility, will enable Meharry researchers to analyze massive healthcare data through machine learning and other artificial intelligence algorithms.

An important objective in developing the diverse data ecosystem is to adapt it to common data model standards to improve interoperability and integration of clinical and observational data into biomedical science. “Moving to the common data model enables us to participate in the national Clinical Research Network, ensuring that our diverse dataset, that features underrepresented populations, can enhance the representation of minorities and underserved in other datasets that improved data diversity at the national level clinical research, and eventually better health outcomes for underrepresented

Education for a career with AI in Healthcare

Despite these concerns, there is clear momentum for continued development of AI solutions for healthcare. This progress also brings strong career opportunities for tech professionals with the necessary background in data science. Meharry School of Applied Computational Sciences offers master’s and doctoral programs in biomedical data science that will prepare students for a career related to AI and healthcare. As an HBCU, Meharry also seeks to graduate professionals who both represent underrepresented populations and understand the importance of diverse data and voices in AI research.

The programs combine math, statistics and computer science skills like programming. A background in healthcare can provide valuable insights into specific challenges facing the industry.

Specific coursework for a career in AI and healthcare might include topics such as:

  • Machine learning and deep learning
  • Natural language processing
  • Visualization and unstructured data analysis
  • Data management and foundations for data science
  • Ethics in data science
  • Computational software engineering
  • Biostatistics

In addition to formal education, individuals pursuing a career in AI and healthcare should also seek out opportunities for hands-on experience. Meharry SACS integrates opportunities to work with real-world data throughout its curriculum. The master’s program concludes with a capstone project focused on an issue selected by each student. The Ph.D. program also features projects and, through the dissertation, dedicated research that could lead to new technology solutions or novel data analysis methods that can impact health care.


Brumfiel, Geoff. “Doctors are drowning in paperwork. Some companies claim AI can help.” NPR. April 5, 2023. Accessed April 6, 2023.

Faguy, Anna. “Bill Gates thinks AI will revolutionize healthcare for world’s poorest.” Forbes. March 21, 2023. Accessed April 6, 2023.

Kaushai, Amit; Altman, Russ; Langlotz, Curt. “Health care AI systems are biased.” Scientific American. November 17, 2020. Accessed April 6, 2023.

Tyson, Alec; Pasquini, Giancarlo; Spencer, Alison; and Funk, Cary. “0% of Americans Would Be Uncomfortable With Provider Relying on AI in Their Own Health Care.” Pew Research Center. February, 22, 2023. Accessed April 6, 2023.

Wong, Carissa. “AI ‘fairness’ research held back by lack of diversity.” Nature. March 30, 2023. Accessed April 6, 2023.

Next steps

The School of Applied Computational Sciences offers two master’s degrees—M.S. Data Science and M.S. Biomedical Data Science. The Ph.D. in Biomedical Data Science program is also available. Download our program brochures and get ready to learn the skills and methods you need to enjoy a rewarding career in data science.


You can also read about the skills and methodologies you will learn through our M.S. Data ScienceM.S. Biomedical Data Science and Ph.D. Biomedical Data Science courses.

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