Prof.Dr. Nisam Rahman A
The artificial intelligence in diagnostics market size was valued at USD 576.3 million in 2021 and is projected to grow at a compound annual growth rate (CAGR) of 26.3% from 2022 to 2030. The prevalence of AI technologies in the diagnostic process has increased in various verticals to achieve higher operational & clinical outcomes, which is a key contributing factor to its growth. The key driving forces behind this growth are reduction in hardware costs, availability of data and accelerated research in AI algorithms/models. Overburdened healthcare systems are struggling with the rapidly rising global prevalence of chronic diseases. This is driving the demand for automated and innovative processes in healthcare. Furthermore, the shortage of human resources is also significantly contributing towards the growing demand for AI-powered systems.
The healthcare system is a complex and dynamic environment where medical specialists are continually facing new challenges due to intertwined complex factors affecting it. The clinical interpretation of medical information is a cognitively challenging task. Though AI offers uniform and scalable technical infrastructure across different demographic, AI also significantly reduces the subjective and opinion of medical practitioners. There are still many challenges faced by practitioners while adopting AI powered solutions in day-to-day medical diagnostics. Few of the broadly classified challenges along with the commonly posed research questions/areas are listed below.
| Problem Area | Research Questions/Areas |
| Advancements and Explicability | How SOTA model developments can be applied in disease diagnostics and combined to achieve better results compared with single algorithm approaches?The diseases where AI methods are more favourable for achieving better diagnostic results?To what extent can deep learning algorithms be developed to provide explainable and understandable results for medical experts within the diagnostic process?How can AI transparently outline diagnostic results for strengthening the confidence of healthcarepractitioners in the system? |
| Corroboration and Portability | How can existing AI developments in disease diagnostics be transferred to other diseases and/or conditions?What results do AI approaches achieve in disease diagnostics when analysing deviating data formats, (i.e., data drifts/concept drift) such as X-ray images or ultrasounds?What are the imperative data requirements for using AI in a real-world scenario of disease diagnostics? Which is an ideal suggested real-world scenario suitable for the practical examination of AI within disease diagnostics? |
| Integration and Collaboration | How can AI be integrated into existing technical environments to assist within the diagnostic process? |
| What are the requirements for designing a user interface of an AI-based system to assist with diagnosing diseases? What are the prerequisites for a successful collaboration between medical practitioners and AI in disease diagnostics? To what extent do human-AI teams achieve superior results compared with human medical teams in thediagnostic process? |
Many research works have highlighted the successful implementation of 3D printing, robotics, ultrasounds, magnetic resonance imaging, mammography scans, genomics, computed tomography scans. This all has further paved way for successful diagnostics in several diseases like Cancer, Heart disease, Ebola, Alzheimer, Tuberculosis, stroke, Hypertension, and other Chronic diseases.
The key identified areas where AI can offer benefits in disease diagnostics and reduced costs are as follows:
- Improve accuracy of diagnosis, prognosis, and risk prediction
- Optimize hospital processes such as resource allocation and patient flow.
- Identify patient subgroups for personalized and precision medicine.
- Discover new medical knowledge (clinical guidelines, best practices).
- Automate detection of relevant findings in pathology, radiology, etc.
- Improve quality of care and population health outcomes, while reducing healthcare costs.
- Model and prevent spread of hospital acquired infections.
- Reduce medication errors and adverse events
In nutshell we can say that AI can add value by either automating or augmenting the manual labour of healthcare staff. Many repetitive tasks can be fully automated and hence AI can act as a tool to help healthcare industry perform better at their deliverables and improve patient’s wellbeing. In today’s time the organizations who leverage the benefits of AI technologies to rethink and reimagine their workflows and processes will be the pioneers in their domains by creating truly intelligent and sustainable healthcare systems.