Artificial Intelligence in Diagnostics Market Trends

Growth Drivers

• Growing need for early diagnosis: The increasing need for early-stage diagnosis of diseases and the growing prevalence of several chronic ailments have increased the demand for AI in diagnostics. AI enhances the speed and precision of diagnosis, offering critical advantages in disease prevention and management. Bayer's launch of Calantic digital solutions in June 2022 is a response to this trend, as it supports radiologists in handling their growing workload with simplified lesion detection. AI integrated into early diagnostics helps clinicians arrive at more informed conclusions which further drives the adoption of AI in diagnostics.
• Technology development in AI algorithms: AI systems continue to evolve and expand their capabilities for more precise image analysis and diagnostics. For instance, the release of Google Cloud's Medical Imaging Suite in October 2022 improved healthcare data access and interoperability, bringing faster and more accurate image diagnosis, hence improving patient outcomes. Such launches and advancements are shifting the way medical data is processed and analyzed, leading to more concrete diagnostic results.
• Increasing investments in healthcare AI: Private and public sector investments in the segment of healthcare AI have gained tremendous momentum, hence fueling innovation in diagnostic machinery. In January 2023, Intel Corporation introduced 4th Gen Intel Xeon Scalable processors for enhanced performance of data centers by strengthening AI-powered diagnostics within healthcare systems. Increased funding by companies opens opportunities to explore new uses of AI in diagnostic settings, driving artificial intelligence in diagnostics market growth.

Challenges

•  Regulatory and ethical challenges: The adoption of AI in medical diagnostics is strictly regulated, focusing on the protection of personal information in regard to ethical use by patients. The Secretary of Health and Human Services (HHS) launched a new executive order on AI that requires the development of frameworks on responsible AI deployment, assures nondiscrimination laws are followed, and that the electronic health record is more open. It becomes a challenge for companies seeking to scale their AI solutions in healthcare to navigate these regulations.
• Lack of skilled workforce: AI in diagnostics requires a certain level of expertise, which is currently lacking in the industry. This has slowed down the adoption speed of AI technologies in specific geographies. For instance, a World Health Organization report argued that there is a growing gap pertaining to skilled professionals who can manage AI technologies, which is eventually impacting its wide adoption. Addressing the issue of the competency gap would go a long way in global expansion for AI-enabled diagnostic tools in healthcare.