While artificial intelligence (AI) has been introduced widely into healthcare, adoption at scale across the industry is still in its infancy. As a direct result of COVID-19, there has been an increased interest from both medical enterprises and data practitioners to find solutions to healthcare problems using technology and AI.
Acumen Research and Consulting predicts the global AI in healthcare market will surpass $8 billion by 2026. As AI and big data are becoming increasingly prevalent in healthcare, these recent and emerging trends will create lasting changes in the industry.
Digital pathology saw rapid adoption of AI in 2020 as a means to continue providing quality, necessary care for patients with pre-existing conditions and diseases while short-staffed medical facilities were overwhelmed with COVID patients. The increased need for remote work and treatment led to the CMA and FDA issuing temporary policies that have made digital pathology cheaper and more flexible by allowing for more remote diagnosis and relaxing regulations on whole slide imaging devices.
There has been considerable movement in AI research related to digital pathology since the pandemic began. Recently, in December 2021, clinical pathology company Sonic Healthcare was part of a $97 million funding round of AI company Harrison.ai, and announced a joint venture “to co-develop and commercialize new clinical AI solutions in pathology.”
Grand View Research reports that due to the increased demand for advanced diagnostics as a result of the climbing prevalence of chronic diseases, the global digital pathology market size is expected to reach $1.74 billion by 2030. The market size was at $311.8 million as of 2021.
Democratization of AI
Industry analysts predict continued growth in the worldwide low-code and no-code development technologies markets, citing democratization as one of the major drivers. A 2021 Gartner study forecasts the worldwide market will total $29 billion by 2025.
General AI democratization coupled with significant research and development in healthcare AI will inevitably lead to wider industry adoption. AI tools will become more accessible to medical professionals that are not highly specialized data practitioners, creating more synergy throughout diagnosis, care and data analysis by reducing the need for intervention by software professionals.
AI as a Service (AIaaS) companies have already begun partnered work with healthcare institutions to offer build-your-own AI models and low-code algorithms that better suit industry-specific needs for analysis and reporting.
Use of Patient Data
AI unlocks wide-reaching potential for better care and treatment by allowing medical professionals to efficiently analyze and compare massive amounts of data. As more healthcare institutions digitize their new and archived patient records, there is an ever-growing amount of medical data that can be used to identify patterns and similarities between patients.
Healthcare providers can employ AI to identify patterns of data that can signal a change in patient status or risk of developing certain diseases. There are already cases that prove AI has led to more accurate and faster diagnosis in cases of COVID, tuberculosis, Alzheimer’s and more, often weeks prior to patients experiencing traditionally-expected symptoms.
AI algorithms can also assist in drug development by allowing researchers to identify the most ideal patients to participate in the research and trial processes, significantly reducing time, cost and error in development.
Our previous blog posts discuss how leveraging data can fuel pharma innovation and reduce health disparities and improve health equality.
While still at the precipice of widespread AI adoption, innovation in healthcare is profound. To learn more about how TripleBlind can help further data collaboration and analysis for healthcare enterprises, please contact us today.