The medical research field is transformed daily with AI adoption. The arrival of artificial intelligence has also ushered in faster discoveries and improved accuracy. With its ability to process large data, AI helps researchers better identify patterns, simplify hypotheses, and interpret sophisticated medical data. Machine learning (ML) plays a crucial role in data structuring and interpretation, and thus research is less costly and more precise.
Through the automation of time-consuming processes, AI allows scientists to focus on innovation, leading to drug discovery breakthroughs, diagnostic breakthroughs, and treatment planning breakthroughs. With further advancements in AI, its influence on medical research will continue to grow, determining the future of medicine and enhancing patient outcomes. The article examines the application of AI in healthcare research.
Enhancing study design in clinical trials
AI is transforming clinical trial design. Earlier, researchers used statistical models and manual data analysis to determine trial parameters. But today, AI-based tools browse through large datasets to choose the best study design. Machine learning (ML) algorithms can review previous clinical trials, detect the most important patterns, and suggest the best sample sizes and patient populations.
For example, MIT scientists developed an AI system that is capable of predicting the outcome of clinical trials from past study data. It minimises trial failure and boosts efficiency. In India, AI-powered platforms are rationalising trial protocols for speedy approval and decreasing redundant exposure to patients. These enhancements lead to quicker drug development and greater credibility in clinical research.
AI in drug discovery and development
Developing new drugs for diseases and conditions is generally an expensive process. At times, drug development can even take more than a decade. AI is shortening this period in half by discovering potential drug targets much faster. AI algorithms scan molecular shapes and predict how compounds of different forms will interact with human cells, eliminating the requirement for excessive lab work.
One example of this is DeepMind’s AlphaFold, which is a machine learning system predicting protein structures with high accuracy. This has changed scientists’ ability to understand diseases and design new cures. In India, the Council of Scientific and Industrial Research (CSIR) has signed a framework agreement with Aganitha (private sector firm) to use generative AI to conduct therapeutic research and drug development for diseases like TB, malaria, and other neurological conditions. Further, pharmaceutical companies are using AI-powered drug discovery platforms to re-profile existing drugs and discover new uses. Repurposing drugs is making it easier to find treatments for orphan diseases and disorders with limited therapeutic options.
Increasing patient enrollment in clinical trials
Recruitment of patients into clinical trials is one of the biggest challenges of medical research. AI is tackling this by making it simpler to find suitable participants. Machine learning algorithms search through electronic health records (EHRs) to find patients with the right medical history and condition to be matched with clinical trials.
One such case in point is Deep6 AI, which employs natural language processing (NLP) technology for screening patient records and identifying possible trial participants in a matter of minutes. This reduces recruitment time and enables trials to access a varied and relevant patient population. In India, AI-powered recruitment platforms are being connected to hospital databases with the objective of streamlining the selection process and improving trial enrollment rates.
AI in medical imaging and diagnostics
Medical imaging is an essential part of medical research and remains one of the prominent aspects impacted by AI. AI is transforming efficiency and precision when it comes to diagnosing sickness. AI technology employing image recognition processes X-rays, MRIs, and CT scans and detects irregularities more effectively than human radiologists in many cases.
For example, Google’s DeepMind has created AI models that can diagnose eye diseases from retinal scans with the same accuracy as the best ophthalmologists. Indian startups such as Qure.ai are employing AI to identify tuberculosis (TB) in chest X-rays more quickly and accurately in remote locations of countries like Haiti.
Improve data analysis and safety monitoring
Clinical trials generate massive amounts of data, and hence its analysis is a complicated process. AI streamlines this by identifying patterns, identifying exceptions, and projecting outcomes in real-time. Data-driven decisions powered by AI aid investigators in arriving at faster choices, accelerating the trials.
In the US, the FDA has implemented AI models to track drug safety, predicting adverse reactions ahead of time with algorithms. Indian regulators are also investigating AI-based monitoring systems to verify the safety and efficacy of new drugs. AI enhances research results’ validity and minimises errors with automated analysis.
Influence on genomics and precision medicine
Genomic research has seen tremendous advancement with AI-powered analytics. AI can sequence and examine DNA information more quickly, detecting genetic markers of disease. Researchers can then develop specially tailored treatment protocols to suit the patient.
For example, IBM Watson is being used to deliver customised treatment advice on the basis of the genetics of a patient. Genomic studies involving AI are helping in the prediction of genetic predisposition to diseases like diabetes and heart disease in India. The ability of AI in sifting through massive genetics databases is making it possible to have faster and better treatment.
Grasping the future of AI in medical research
The future of medical AI research is promising. With ever more advanced algorithms, we can expect to see speeding-up drug discovery, improved clinical trials, and improved diagnostics. AI-enabled wearables can also do more to track patient health in real time and provide continuous data for preventive medicine.
In India, medical research with AI is also developing very rapidly. Private health centres and hospitals in India are investing in AI-based research in an effort to combat life-threatening illnesses. The capability of AI to read information, make predictions, and automate processes will also shape the future of medical research.
To conclude
While AI is already picking up pace in companies like NBFCs and online marketplaces, it is also demonstrating its significance in the health sector. AI is revolutionising the practice of medicine by speeding up drug discovery, making it more cost-effective and economical. AI is optimising clinical trial designs, minimising the time for enrolling patients, facilitating easy analysis of data, and allowing researchers to improve their techniques.
With ethics, bias, and transparency concerns, regulatory agencies are making an effort to encourage the use of AI responsibly. With the advancement of AI, its future applications can be adaptive clinical trials, personalised research models, and convergence with upcoming technologies such as blockchain and IoT. As everything keeps improving, AI will be a major innovation driver in medical research and providing more effective treatments and healthier outcomes for patients globally.
