Sep 15th, 2024
Unlocking the Power of Generative AI in Healthcare Software Development
Generative AI (artificial intelligence that can create new content by analyzing and leveraging reference data) has already been embraced across industries as diverse as finance, entertainment, tech, and sports. The healthcare software development industry can also see great benefit from this technology, as it alleviates pain points and bottlenecks in client/physician interactions, data collection, personalized care, and more.
With generative AI gaining momentum throughout 2024, we’re seeing more businesses adopt and derive value from this incredible new technology.
What is Generative AI in Healthcare Software Development?
Generative AI in healthcare involves employing sophisticated artificial intelligence models designed to address the unique challenges and needs of medical practice and research. This technology can be applied to numerous areas of healthcare technology, such as documentation, coding, diagnostic decision support, administrative tasks, information gathering, and translation.
U.S Healthcare Generative AI Market Statistics
According to Polaris Market Research, U.S. healthcare generative AI market is projected to expand from USD 705.32 million in 2024 to USD 8,131.58 million by 2032, with an expected compound annual growth rate (CAGR) of 35.7% over the forecast period.
According to a survey conducted by Deloitte, 75% of top health care companies are either testing or intending to expand the use of Generative AI throughout their organizations.
Leaders recognize the potential of Generative AI to enhance efficiencies (92%) and facilitate faster decision-making (65%).
The Benefits and Applications of Generative AI in Healthcare Software Development
There are many benefits to applying Gen AI to healthcare software development, across all sectors of the industry, from insurance offices to urgent care facilities.
Patient Diagnosis
AI has been shown to accurately diagnose chronic illnesses with great accuracy. Generative AI excels at analyzing unstructured data sets, meaning it can intake a patient’s family history, medical records, and current symptoms and accurately diagnose potential health risks and medical issues.
Personalized Treatment
Beyond just accurately diagnosing a patient’s risk or condition, Gen AI can also create personalized treatment plans based on that diagnosis. As biotech improves and wearable health trackers become more prevalent, diagnosing and creating personalized care will become even more efficient as a wealth of patient data will be available.
Analysis
Image analysis is another area in which Gen AI is incredibly useful. Detecting abnormalities in x-rays and MRIs is just one example of how AI is being applied in this area. Researchers at Stanford have developed an AI that is able to detect 14 different pathologies in an x-ray in seconds.
Risk Prediction
When it comes to insurance and treatment plans, Generative AI can be used to accurately forecast risk. Not only that, it can also be used to predict risk for catastrophic health events at a national or even global level. Epidemics and pandemics could be more accurately predicted, giving governments more time to prepare. AI can also help authorities to track the way a viral strain mutates, or even predict how it might mutate.
Drug Discovery and Development
The precision, safety and efficiency of drug development can be improved by Generative AI, which can quickly analyze large datasets of potential subjects for clinical trials and determine suitable candidates. It can predict potential side effects and drug interactions, and more easily monitor subjects and gather data during trials.
The Challenges of using Generative AI in Healthcare Software Development
Of course, utilizing AI in healthcare software development is not without some risks, and not everyone is excited about the prospect of generative AI finding its way into their treatment.
Data Protection and Privacy
AI relies on data, and patient data is incredibly sensitive. In order to effectively use AI in the healthcare industry, special consideration must be made for the sensitivity of the data it is analyzing. Third-party data vendors could be exposed to breaches, patients could lose control of their personal health data, and the vast amount of data needed to train generative AIs could result in a lot of patients’ records being memorized by a system they have little control over.
Racial and Gender Bias
It’s well known by now that artificially intelligent systems suffer from racial and gender bias, just as many other large institutions do. The data used to train AIs is often biased, simply because the data lacks records for people of certain genders, races, or ethnicities. This results in baked-in biases that are then solidified and propagated by the AI that was trained on that data.
Backlash and Public Comfort Level
According to a Pew Research poll, at least 60% of Americans would be “uncomfortable” if their primary care physician used AI to make a diagnosis or treatment recommendation. As industries rush to embrace the new technology, many in the public are wary of a technology they don’t understand (and which many experts don’t understand either.)
Bottom Line
The potential applications of AI in healthcare software development are many and varied, but the risks are not to be ignored. As the movement to incorporate AI into all sectors of industry gains momentum throughout 2024, it will be important to approach this technology with caution and pragmatism. Setting realistic expectations, considering patient needs, and prioritizing care over profit will ensure we move forward responsibly and ethically.
