Market Challenges And Opportunities

Increasing Number of Startups, Collaborations, and Venture Capitalist Funding: The AI healthcare market is growing at a rapid pace, constantly showing untapped opportunities for entrepreneurs. As per a DataRoot Labs article in September 2020, over the recent years, AI innovation in the healthcare space has been overwhelming and the market is growing from USD 1,110.7 Mn in 2022 to USD 5,773.6 Mn in 2030 at a CAGR of 21.2%. The healthcare space holds large volumes of data from imaging, genomics, and diagnostics, which has led to the emergence of startups over recent years. In addition, the ongoing COVID-19 pandemic has given digital health a push, ranging from telehealth diagnostics to rapid testing, with startups receiving motivation & boosts to their product deliveries. Startups in personalized and connected healthcare are receiving recognition by enabling patients to track their health, allowing them to choose & manage their healthcare services.

Integration AI into the medical system is expected to drive market growth: Another factor which is driving the growth of the global artificial intelligence (AI) diagnostics market is the integration of AI in to the medical, and healthcare sector. For instance, Integration of AI into electronic medical records can provide multiple benefits and can significantly improve diagnostic algorithms, decision support, interoperability, flexibility, capturing physician-patient conversation. For instance, Google collaborated with delivery networks for prediction modules to alert health risks such as heart failure. Machine learning solutions for IBM Watson, AllScripts, and Change Healthcare are using healthcare data to recommend personalized treatment options. Amazon Web Services provides a cloud computing tool that uses AI to extract and index data from clinical notes.

Saykara’s virtual assistant tool, Kara, uses voice recognition, language processing, and machine learning models to capture physician-patient conversation & turn it into notes stored in EMRs.

Market players trying to improve the alignment between EMRs and AI technologies are limited, leaving scope for untapped opportunities, to be leveraged by innovators and startups. Future EMRs can be developed with scope for integration of data captured from telehealth services and wearable’s. This can allow data to be used in clinical studies, home health tracking, advanced diagnostics, and prediction of health risk events.

Artificial Intelligence (AI) Diagnostics Market Opportunities:

Managing and preventing or delaying the onset of chronic diseases can be a taxing & time-consuming task. AI-based data-driven technologies are enabling healthcare personnel to accurate and timely treatment, enabling a more proactive & preventive approach rather than a reactive one. AI can help provide an integrated care plan to better manage chronic ailments. Image processing, diagnostic findings, deep learning, and neural networks have experienced advancements over the past few years. For instance, in December 2020, the two companies partnered and combined resources in medical image analysis for pulmonary diseases, to develop an end-to-end AI-based solution for detecting and treating pulmonary embolism, based on historical images.

Restraints & Challenges:

Procurement costs and maintenance is expected to hinder market growth

Global Artificial Intelligence (AI) Diagnostics Market Restraints:

Procurement costs and maintenance is expected to hinder market growth: The AI-based healthcare market includes high procurement costs and requires major initial capital, followed by upgradation & maintenance costs. Financially established players, such as hospitals are major investors in the market. With low government spending on developing and introducing AI-based technologies, most of the expenses are borne directly by the private consumer. For instance, the European government has yet to long-term investment-intensive strategies in adopting AI-based healthcare solutions. Procurement of hardware and software solutions to integrate AI into the healthcare setting involves major costs due to the complexity of engineering in the system. Furthermore, an average repair or maintenance of an AI-based system can be extremely expensive. These systems need constant upgrades to keep up with changing scenarios involving such costs. In addition, substantial investments are required in digitalization and training programs. Healthcare systems have already made significant capital investments in rolling out electronic health record systems and to acquire & integrate AI solutions in healthcare settings, which is likely to be an expensive ordeal.