Healthcare tech is amid a complete transformation brought about by Artificial Intelligence (AI). Advancements in AI in the last decade have made it a revolutionary force that can reshape the landscape of healthcare and care quality. AI, in its various integrations and use cases has already reduced administrative burdens, streamlined process, improved care quality and patient experience. The market for AI, too, is on the rise. The AI healthcare market is projected to be valued at $187 bn in 2030. This is a remarkable increase from its 2021 valuation of $11 bn (1).
Further, at a time when global venture investments are down overall, only healthcare and AI are exceptions (2).
The future, therefore, looks promising. There could be more neat integrations of AI in various aspects of healthcare delivery, administration, payments and more. Efficiencies will be built wherever feasible. Even if we set the future aside for the moment, AI is already boosting efficiency, saving costs and time, and empowering healthcare professionals to serve patients better. Before we delve into that, let us turn the clock back and look at the evolution of AI in healthcare IT so far.
Looking back at the Evolution of AI in healthcare IT
The journey of AI commenced with its formal establishment in the 1950s (3). In the 60s, early AI programs were used for medical problem-solving and diagnosis (4). In the subsequent decades, there were rule-based and expert systems, like MYCIN in the 70s (5). These set the stage for AI’s role in diagnostic decision-making. The 90s saw the integration of machine learning techniques into medical apps, and the rise of health informatics and deep learnings. One good example was IBM’s Watson, and the use of AI in diagnostic imaging and drug discovery.
From 2015 to 2020, the first AI-based diagnostic device won FDA (Food and Drug Administration) approval (6). As we run through the 2020s and even beyond, AI has decidedly taken centre stage in health-tech – reaching into areas such as personalized medicine, predictive analytics, and remote patient monitoring.
Current State: Practical AI Contributions in the Health-tech Industry
Let’s look at some of the current practical AI contributions in the industry to understand how far the technology has evolved and its benefits.
1. Medical Imaging and Diagnostics:
In radiology, AI algorithms assist in detecting anomalies in X-rays, MRIs, and CT scans by quickly analyzing large datasets and expediting diagnosis. This contributes to the early detection of life-threatening diseases. For instance, PathAI helps pathologists identify and diagnose diseases by analyzing pathology slides with greater accuracy using AI.
2. Predictive Analytics for Patient Outcomes:
With AI-powered predictive analytics, healthcare providers can anticipate and manage patient outcomes by analyzing historical data. AI models can predict potential complications, readmission risks, and recommend personalized treatment plans. All these measures contribute to enhanced patient care and optimal resource allocation. For instance, AI algorithms developed by Google’s DeepMind have helped predict patient deterioration, prompting early intervention by providers.
3. Drug Discovery and Development:
In drug discovery, AI is being used to expedite things by quickly analyzing vast databases, to identify potential drug candidates, and predicting their effectiveness. For instance, IBM Watson for Drug Discovery studies scientific literature, clinical trial data and other sources to aid researchers in the discovery process.
4. Operational Efficiency and Workflow Optimization:
Healthcare organizations are leveraging AI to optimize internal processes and improve operational efficiency. AI algorithms assist in resource allocation, appointment scheduling, and supply chain management, contributing to cost reduction and enhanced efficiency. For example, the company Tempus uses AI to analyze clinical and molecular data, helping healthcare providers make data-driven decisions for personalized cancer care.
The Future of AI in Healthcare
With continuous advancements in AI, the future potential is multi-faceted, with expected advancements in other areas such as Personalized Medicine, Remote Patient Monitoring and Robot-Assisted Surgeries. Let us investigate some of the advancements we can expect in the future.
- Personalized Medicine: AI can analyze individual patient data and characteristics to create tailored treatment plans, which can lead to more effective and targeted therapies.
- Remote Patient Monitoring: AI-powered wearables and sensors can monitor patients remotely, enabling early detection of health issues and improved disease management.
- Robot-Assisted Surgery: Robotic surgery systems are improving surgical precision and minimizing risks. As AI continues to advance, these systems are expected to become even more sophisticated and widely used.
Conclusion: A Brighter Future for Healthcare
AI integration in healthcare promises more efficient, patient-centric, and technologically advanced healthcare systems. Overall, it is a net positive for all stakeholders and contributes to better healthcare outcomes for all.
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References:
- https://www.statista.com/statistics/1334826/ai-in-healthcare-market-size-worldwide/
- https://www.fiercehealthcare.com/finance/funding-down-56-healthcare-ai-stand-apart-market-volatility
- https://www.cedars-sinai.org/discoveries/ai-ascendance-in-medicine.html
- https://deepblue.lib.umich.edu/bitstream/handle/2027.42/30758/0000409.pdf%3Bjsessionid%3D70A227C7B0C5A1B04090514F8977501C?sequence%3D1
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/
- https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30292-2/fulltext#:~:text=The%20number%20of%20approved%20AI%2FML%2Dbased%20medical%20devices%20by,the%20FDA%20(figure%202)
