How can AI help medical research
Medical research is the backbone of medical evolution; it is where innovation and scientific breakthroughs occur. With that in mind, it is no wonder people are asking about the role of AI in this and how AI will help medical research now and in the future. Based on our experience developing custom AI software solutions, we thought we would have a detailed view of its current applications and try to predict how it will impact the industry and medical research as a process.
Medical Imaging
Medical imaging is one of the fields where AI can truly shine because of its ability to identify patterns. Imaging is used in diagnostics and pathology, and it’s essential for a good medical service, but it often requires time and a high level of expertise from clinicians.
Because AI systems can learn from millions of data points, they can become extremely valuable in identifying patterns in medical imaging. Thus, they speed up the process for clinicians and even increase efficiency as a “second opinion,” especially in scenarios where staff is overloaded.
Drug discovery and development
Drug development is often a highly expensive endeavor, and it can take decades to reach a final product without knowing whether it will ever hit the shelves. Because it’s such a high-risk process, pharmaceutical companies are looking for alternative tools and options to increase the efficiency of the process.
AI and ML (Machine Learning) are becoming increasingly common in drug development because they can drastically improve the efficiency of the process while discovering patterns and flagging potential flaws that are difficult to observe by the human eye.
These systems will greatly impact drug development as they can perform complex analysis tasks over large amounts of data and run simulations to find suitable molecules and compounds.
Medical research and clinical trials
Medical research might be the most important long-term activity in the entire field because it’s the root of real innovation, the “birthplace” of new life-saving medical procedures and drugs. Besides that, it’s also one of the most expensive processes and time-consuming because of the large number of data sets needed, the highly specialized experts, and the potential number of trials and errors needed.
AI systems can streamline data collection and processing, accelerating clinical trials and improving trial design. Furthermore, they pose great potential for data analysis for clinical trials and research because they can process enormous data sets, detect patterns, and predict outcomes.
Clinical decision support (CDS)
Clinical decision-making efforts are integrated into electronic health records (EHRs). AI can help extract data from these systems and other sources.
The huge advantage of AI is that it can work with a large amount of data while allowing key people to have a granular view, thus improving decision-making.
AI also has the capability to use pattern recognition, meaning that CDS systems can now include risk stratification and predictive analysis. This breakthrough allows clinicians to deliver personalized treatment plans, especially for high-risk situations such as chronic diseases.
Electronic Health Records
EHR adoption has been happening for some time, and it poses a real benefit for both clinicians and patients alike. One of the main struggles until now is that even though clinicians have access to patients' electronic health records, they are quite difficult to manage, extract the needed data, and make predictions based on them.
Luckily, AI can and will most likely change the game because of its advanced analysis capabilities. It will extract the exact information clinicians need in real-time, and maybe more importantly, it will be able to present it in natural language using NLP (Natural Language Processing). In other words, clinicians will be able to ask for different critical information naturally and get replies naturally in real-time.
Another critical point is the modernization process, transitioning from a legacy system (often pen and paper) to a digital record. AI’s image recognition and text interpretation can drastically speed up the process and transform classical records into digital ones.
Genomics
Genetic data can reveal much about a patient, such as potential health concerns or hereditary diseases. Access to the patient genome sequence is exciting, but the present problem is how to use it in the real world.
With the use of AI, the information stored in the genome sequence can be processed and combined with the EHR data to provide an even broader and more precise image of the patient's health status. Maybe more importantly, it can add that missing puzzle piece to predicting potential outcomes based on treatment and medication.
Hospital Management
Health system operations and revenue cycles are critical to hospitals, especially in countries like the US. AI can be integrated into existing processes to make better predictions, analyze data, and improve management decision-making.
Another important topic is managing resources across a hospital to improve the efficiency of the medical act. A tailored AI system can help with capacity management by monitoring staff shortages, surgical schedules, staff restrictions, and surgeon preferences to ensure a smoother medical act and better resource allocation.
For example, the inconsistent use of the operating room combined with the surgeons' preferences can create significant downtime (as the equipment sits idle). To tackle this, AI-based software solutions can use data such as the surgery type, length, equipment needed, and resources to help staff streamline the process.
Patient Management
We tackled the subject of AI chatbots and the significant impact they can have. This is one of the areas in which they can provide real help to patients and clinicians alike. Traditionally, there are solutions in which patients and their clinicians communicate. Still, the issue with them is that often, because of the workload, both parties can become frustrated - patients because of the lack or delay of the response and clinicians because they don’t have enough time for the medical act itself and get lost in administrative tasks.
AI chatbots can be built to access electronic health records and provide a useful first interaction. They could respond to patients in a natural way (through NLP) to most questions and give personalized replies based on their medical records. This means that only the most important messages or requests will be sent to the clinician, helping both parties in the process.
Robotics
Robotics are mostly used for surgical purposes; for example, robotic arms are used by surgeons for precision interventions. While the inclusion of AI will mostly not happen as fast as in other fields, it still has its place in the future of medical robotics. As we might not see AI systems controlling the robotic arms in surgery right away, there are cases where it’s already implemented in pre-operative tasks, such as risk monitoring and analysis of the patient's current condition to better prepare the operating team for the intervention.
Epidemiology
Statistical modeling is currently being used to predict future disease outbreaks. AI will be able to improve these systems to provide localized real-time predictions of a current epidemiologic situation as well as being able to make predictions of the outbreak timing, giving authorities more time to react and create countermeasures. Furthermore, as with all other categories, they can speed up the modeling process because they can process high amounts of data, identify patterns, make predictions, and help researchers manage a higher volume of potentially dangerous viruses or bacteria.
Conclusion
In terms of raw impact, healthcare and medical research are the domains where AI can deliver the most because of its positive impact on millions of people's well-being. AI is already being used and experimented with. Still, because of the field's complex nature, extremely tight regulations, and broad implications, it’s being implemented progressively.
At Ontegra, we believe that well-designed, custom AI software solutions will decisively change the field in the coming years, with high responsibility and privacy in mind. Our company has a dedicated AI software solutions department with extensive know-how and experience that can help your company incorporate AI into its operations and more. We also developed our own NLP API and designed on-premise AI systems from the ground up for improved accuracy, privacy, and efficiency. If you want to discover how AI can improve your company's outcome, message us, and we will contact you.
Article written by Iulia Filimon, NLP Engineer
Iulia designs and develops algorithms and models that enable computers to understand, interpret, and generate human language. She participates in building the first innovative AI company's solutions and exposing them to clients.
