Role of AI and ML in life science

Artificial Intelligence (AI) has been accelerating the life-sciences industry by making scientific breakthroughs over a few years. AI has successfully overcome significant challenges in organizing all the data systematically and using it at its potential to develop extensive healthcare information.

Starting from integrating human health data and segregating clinical records to improve research. Smart technologies are used in life science through various applications such as mass spectrometry and liquid chromatography to provide better diagnoses and effective treatments for patients.

AI and ML in life Science

According to Clarivate Analytics, drug, and medical device researchers are challenged more than ever to access and analyze data quickly and efficiently. This growing ecosystem needs a fast development lifecycle which could be solved through AL. 

So, let’s dive deep into the role of AL and ML in life sciences:

Personalized Medicine

Currently, most medical providers are following a standard medicine dosing prescription. The ‘One size fits all’ approach has led to incorrect dosages for patients. There is research in progress using ML and predictive analytics to customize treatment for every patient based on every individual’s unique health history. 

With AI platforms, we can digitize all the patient’s health records and get optimized diagnosis and treatment protocols. With a continuous data monitoring tool, AI will enable doctors to adjust the prescription dose, revise the recovery therapy, and suggest a more effective medication.

Advanced Radiotherapy

Radiotherapy is one of the most critical steps in cancer treatment. It is estimated that nearly 50% of all cancer patients have to undergo radiotherapy for their treatment plan. AI has significantly augmented the ability to localize the treatment area accurately.

Google’s AI research arm ‘DeepMind’ in collaboration with University College London Hospital (UCLH), has been working on ML-based radiotherapy treatment since 2016 to improve the scans available for radiotherapists. ML algorithms will help us differentiate healthy tissues from cancerous ones for minimum damage to healthy cells. 

This simple segregation through ML will benefit the radiotherapy procedure and diagnostic processes such as medical imaging, patient simulation, treatment planning, radiation, and quality assurance.  

Robotic Surgery

Robotic surgery is an upcoming new field that has been observed and widely accepted worldwide. Now surgeries can be performed on delicate and inaccessible body parts that were not possible previously by using the robot. Once trained, a robot is competent enough to perform numerous operations consistently and accurately without any human errors. 

Robotic surgeries are presently offered in several specialties, such as cardiothoracic, gastrointestinal, otolaryngology, gynecologic oncology, and urologic surgery. With AL, everyone can receive state-of-the-art treatments with less downtime and more meticulous care.

Robotic surgery treats patients with less risk of infection, less blood loss, fewer blood transfusions, less pain, shorter hospital stays, and faster recovery.

Challenges

Artificial intelligence (AI) has become an intricate part of the decision-making for healthcare and pharmaceutical organizations to reduce risk factors and optimize bio-medical data. However, it is facing particular challenges in its implementation and adaptation due to the following reasons:

1. Untapped Data: The access to life sciences data is merely 52% currently. This lack of access due to its heterogeneity and unstructured data format creates a barrier to using AI technology.
2. Lack of Skill Set: A recent survey by Pistoia Alliance showed that lack of skill set is as high as 44% in the industry, another major hurdle faced in the adaptation stage.
3. Data Quality: The basic requirement for obtaining quality analysis is working on consistent and clean data in the AI platforms. Data quality is one of the biggest blockades to using AI in drug design.
4. Data Privacy: For AI to attain its full potential, it is crucial to set standard data privacy rules around it. Starting from the conceptualizing phase of AI algorithms, we need a concrete data framework to ensure the data privacy of all stakeholders.
5. Lack of Sync: Another challenge in AI adaptation is the need for more synchronicity between the health system and the company that develops electronic health record (EHR) systems.

What’s Next

As per Pistoia Alliance’s AI Center of Excellence, the life science industry is keenly interested in AI adoption. However, the above-mentioned issues still affect its mass implementation in the field. To make further progress in AI, life sciences companies will need to overcome these barriers to advance AI integration in the coming years.