How AI is Transforming Protein Research and Healthcare

Introduction

 

In the last two months, there have been two important developments in the field of AI in medicine and healthcare: in July, Google DeepMind partnered with the European Molecular Biology Laboratory's European Bioinformatics Institute to create the AlphaFold Protein Structure Database and made it freely available to the scientific community. 

 

Artificial intelligence (AI) simulates human intelligence and automates complex activities. While they strive to mimic the capabilities of the human mind, AI-enabled computers can also outperform it in a variety of ways, particularly by swiftly sifting through massive amounts of big data to detect patterns, anomalies, and trends.

 

Unsurprisingly, AI has numerous prospects in health care, where it may be utilized to improve a wide range of typical medical processes, from disease diagnosis to determining the best treatment plans for individuals suffering from serious illnesses such as cancer. AI-enabled robotic surgical technology can help surgeons execute surgeries more effectively by reducing physical fluctuations and delivering real-time information during the procedure.  

 

AI and Protein Research

 

Protein drug development is time-consuming, labor-intensive, and expensive. Drug developers have always used natural proteins as starting points, followed by the laborious, painstaking process of molding those natural proteins into safe, effective medications.

 

However, the introduction of artificial intelligence (AI), better analytical techniques, and new, inventive approaches to life science research is transforming everything through a process known as generative biology.

 

Similarly to how generative AI systems (such as ChatGPT) generate new data such as text or images from existing inputs, generative biology generates novel protein-based therapeutics with desired architectures and attributes using current protein data inputs.

 

Artificial intelligence (AI) has solved one of biology's greatest challenges: predicting how proteins fold from a chain of amino acids into 3D forms that perform life's functions. This week, the organizers of a protein-folding competition announced that researchers at DeepMind, a UK-based AI company, had achieved their goal. They claim the DeepMind technique will have far-reaching consequences, including substantially accelerating the development of new treatments.

 

"What the DeepMind team has accomplished is extraordinary and will shape the future of structural biology and protein research," says Janet Thornton, emeritus head of the European Bioinformatics Institute. "This is a 50-year-old challenge," says John Moult, a structural biologist at the University of Maryland, Shady Grove, and the competition's co-founder.

 

Use Cases of AI in Healthcare

 

Artificial intelligence (AI) has emerged in healthcare in recent years, resulting in new breakthroughs that promise to improve patient health outcomes and operational efficiencies. From diagnosis to treatment and home care, AI has demonstrated the ability to assist healthcare professionals and patients at all stages of the care continuum. 

 

Here are six real-world instances of AI in healthcare, demonstrating how it may improve experiences for both patients and staff.

 

1. Increasing precision in patient positioning and CT image reconstruction

 

Radiology departments continue to encounter challenges in terms of increasing patient loads and maintaining consistent picture quality while improving operational efficiency. With computed tomography (CT) being one of the most extensively utilized imaging modalities, radiology departments stand to benefit greatly from AI-enabled technologies that expedite CT workflows and improve image quality.

 

2. AI in Drug Discovery

 

The medication development industry is hampered by rising development costs and research that requires thousands of human hours. Each drug's clinical trials cost an estimated $1.3 billion, and only 10% of those treatments are successful in reaching the market. AI is speeding up this process by assisting with drug creation, anticipating side effects, and selecting optimal candidates for clinical trials. 

 

3. Enhancing image capture in MR

 

Magnetic resonance (MR) is another imaging modality that is becoming increasingly significant in the precise diagnosis of medical disorders. The increased usage of MR is increasing the need to efficiently scan more patients and shorten the time between initial scan and final diagnosis. Simultaneously, MR departments seek strategies to increase patient and referring physician satisfaction.

 

4. Improving ultrasound measurements by simplifying them

 

In cardiac care, ultrasound has evolved into a vital tool for visualizing and evaluating a patient's heart function. In fact, it is frequently the initial imaging modality used to diagnose patients. However, the difficulty with ultrasonography is that it is heavily dependent on the person operating the instrument. Certain measurements required for every echo exam can be time-consuming and vary greatly between and within users.

 

We can use AI to replace some of the manual and repetitive labor, as seen in some of the other AI in healthcare examples in this overview. AI-based automatic measures can provide fast and consistent echo quantification, benefiting both patients and staff.

 

5. AI in Healthcare Data Management

 

Highly useful information can sometimes go lost in a forest of trillions of data points. Furthermore, the inability to connect key data points impedes the development of new medications, preventative medicine, and accurate diagnosis. AI's capacity to manage enormous amounts of data allows it to break down data silos and connect information that would otherwise take years to process. This can save time and money on healthcare administrative processes, resulting in more efficient everyday operations and patient experiences. 

 

6. Helping radiologists view pictures faster and more accurately

 

Similarly, AI can help with image segmentation and quantification in radiology, freeing up radiologists' time for higher-level image interpretation. AI can also serve as a second set of eyes, assisting the radiologist's decision-making by pointing out areas of interest or incidental findings that they may have missed.

 

For example, AI systems that scan MR images of the brain for subtle neurological changes over time have been demonstrated to enhance diagnostic accuracy in multiple sclerosis patients by 44% while reducing reading times. Similarly, AI-based lung nodule identification can perform nodule search 26% faster and find 29% of previously overlooked nodules when compared to manual examination.

 

Growth Aspects of AI In Healthcare

 

As with many other industries, AI is poised to transform the health-care environment in the coming years. In addition to enhancing health-care facility operations, patient diagnoses, treatment plan development, and overall health outcomes, AI is predicted to aid in the research and discovery of new medical therapies.

 

The application of artificial intelligence in healthcare is predicted to increase dramatically over the next decade. According to Grand View Research, AI in health care is expected to be valued at $208.2 billion in 2030, which is many times more than its 2022 market size value of $15.4 billion. 

 

The future of AI in health care could include a wide range of tasks, from answering the phone to reviewing medical records, population health trending and analytics, designing therapeutic drugs and devices, reading radiology images, making clinical diagnoses and treatment plans, and even communicating with patients.

 

The future of artificial intelligence in health care involves, A healthcare-focused review of artificial intelligence (AI), natural language processing (NLP), and machine learning (ML).