Medical science liaisons (MSLs) are the scientific face of life science organizations and are responsible for educating healthcare providers (HCPs) on the latest medical developments and technology.1 Artificial intelligence (AI) is altering how healthcare is conducted. As a result, MSLs will need to understand the fundamentals of AI. 

AI uses computers and machines to mimic the human problem-solving and decision-making process. AI transforms data into action and drives decision-making in healthcare and medical affairs. AI was also used during the COVID-19 pandemic to forecast and predict disease trends.3,6

In 2021, the global AI healthcare market was valued at $10.4 billion, and the market will continue to expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. However, the lack of AI experts is a significant obstacle to the growth of the AI market, preventing many businesses from adopting AI technology 

HCPs use AI for patient diagnostics, health services management, predictive medicine, clinical decision-making, and surgery.4,10 Key opinion leaders (KOLs) and HCPs trust medical affairs teams to share valuable knowledge on products, disease states, and AI. 

Whether you are a medical science liaison(MSL), aspiring MSL, or HCP, AI will shape the future of your career. Understanding AI, the subfields of AI, and their functions within medical affairs and healthcare is vital for MSLs and medical affairs leaders. 

This two-part guide to AI for medical science liaisons will clarify these aspects of AI and provide real-world applications of AI. General AI information and machine learning, a subfield of AI, are discussed in part one. 

After reading part one of the guide, you will understand how AI and machine learning play a role in healthcare and medical affairs.

What Is Artificial Intelligence?

AI is the umbrella term for any technology that can reason and adapt based on algorithms and data. AI was initially designed to mimic human intelligence. Algorithms are the basic instructions for AI technology.

All healthcare areas can benefit from AI's application, from diagnosis to treatment. AI tools are expected to streamline and enhance humans' work. HCPs can use AI to help them with administrative tasks, clinical documentation, patient outreach, and specialized support.2  

By automating duties typically managed by HCPs, AI simplifies the lives of patients, HCPs, and hospital administrators. The following companies are developing AI applications for healthcare: 

• Buoy Health

• PathAI

• BenevolentAI

• Babylon

• Spring Health

• Tempus

• Vicarious Surgical

• Accuray

There is a growing interest among the FDA's Center for Devices and Radiological Health (CDRH) in enacting a comprehensive product lifecycle-based regulatory framework for these companies and their new technology. During the lifecycle of the technology, a framework would allow the software to be modified to respond to real-world learning and adaptation.

Currently, AI is used for the following real-world applications in healthcare:8-10

• Robot-assisted surgery 

• Living assistance 

• Disease diagnostics and prediction 

• Imaging

• Clinical trial participation

Some reasons why AI applications are beneficial to hospitals and other healthcare facilities include:10

• When clinicians need data, they can access it immediately.

• Prescriptions can be administered more safely by nurses.

• Patients can stay informed and engaged during hospital stays by communicating with their medical teams.

Hospitals are cooperating with AI companies to develop custom technology to address the needs of patients. For instance, the Mayo Clinic and BD, one of the largest global medical technology companies, collaborated on the Mayo Clinic Platform, a suite of products enabling customers to develop and solve health care issues quicker. The platform can access all the necessary data for clinical decision-making, including demographics, diagnoses, lab tests, clinical notes, and pathology reports.

For every product or platform, a subfield of AI is the driving force behind the technology. How subfields of AI apply to healthcare and medical affairs are explored next. Part one of the guide addresses machine learning, and part two of the guide addresses artificial neural networks (ANNs) and deep learning (DL). 

What Is the Relationship Between Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Deep Learning?

To understand the relationship between artificial intelligence and its subfields, consider them Russian nesting dolls. Machine learning, artificial neural networks (or neural networks), and deep learning are interconnected. The relationship between AI and its subfields is illustrated in Figure 1.

Figure 1: Relationship Between Artificial Intelligence, Machine Learning, Neural Networks, and Deep Learning. Data Source: IBM

What Is Machine Learning?

Machine learning (ML) is a branch of AI using data and algorithms to mimic how humans learn. The purpose of ML is to make predictions and uncover critical insights in data using algorithms. The insights generated by ML are then used to drive decision-making within applications and businesses while ideally impacting key growth metrics. 

In addition to healthcare and medical affairs, ML is used for various purposes, including Netflix’s recommendation page, self-driving cars, and voice recognition. As healthcare increasingly relies on data, ML will continue to play an essential role in medical advancement.5

Healthcare 

ML has been applied to many areas of healthcare, including predicting disease outcomes, identifying patients at high risk of developing certain conditions, and diagnosing diseases before they appear. In addition to predicting and treating disease, ML can contribute to healthcare in the following ways:

• Providing medical imaging and diagnostics

• Discovering and developing new drugs

• Organizing medical records

AI is most urgently needed for disease diagnosis. The use of AI allows HCPs to diagnose diseases earlier and more accurately.9 This includes a variety of in vitro diagnostic methods utilizing biosensors and biochips to diagnose diseases.

For example, genetic tests are an essential diagnostic tool using ML to detect genetic abnormalities.9 Classifying cancer microarray data for cancer diagnosis is one of the new applications.9

Figure 2: 5 AI Applications in Healthcare. The market share of each application is compared in billions of dollars by 2026. Data Source: Harvard Business Review

Medical Affairs 

Medical affairs also uses ML in a variety of ways. Medical affairs increasingly uses data internally to measure key performance indicators (KPI) and analyze outcomes. An MSL's KPIs measure their performance, including their time in the field, the number of medical conferences they attend, and the number of scientific presentations they give. 

ML analyzes how the MSL's actions impact KPIs and accurately calculates an MSL's ROI. KPIs are established and analyzed by customer relationship management (CRM) tools.

ACMA Engage™ is a customer relationship management (CRM) tool designed by medical affairs experts and for MSLs teams. The CRM tool allows MSL teams to gather feedback more quickly and efficiently produce market insights.

ACMA Sentiment™ also employs ML to provide MSLs with real-time insights into all therapeutic areas globally. ACMA Sentiment™ is powered by ML and allows MSL teams to analyze the following insights:

• Customer feedback on products, disease state, and the company

• Share of Voice (SoV) of digital KOLs by therapeutic area

• Strategic planning and anticipating future market needs

• Providing impactful scientific engagement

The ACMA now offers a full suite of digital solutions for MSL teams called ACMA Insights™. Powered by ML, the cloud-based programs provide a range of tools with predictive analytics ensuring life science organizations are optimizing their activities.  

Return to the ACMA blog for part two of the guide to learn more about AI. In part two of the guide, the other subfields of AI, artificial neural networks (ANNs) and deep learning (DL), are explored. The guide will explain the real-world applications of ANN and DL for healthcare and medical affairs. 

To discover more about ACMA Insights™, ACMA’s comprehensive digital solution powered by AI, visit the ACMA Insights™ homepage.

References:

1.Bedenkov A, Moreno C, Agustin L, et al.. Customer Centricity in Medical Affairs Needs Human-centric Artificial Intelligence. Pharmaceutical Medicine. 2021;35(1):21-29. doi:10.1007/s40290-020-00378-1.

2. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare. 2020;25-60. doi:10.1016/B978-0-12-818438-7.00002-2.

3. Comito C, Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artif Intell Med. 2022;128(102286):102286. doi:10.1016/j.artmed.2022.102286.

4.Fazlollahi AM, Bakhaidar M, Alsayegh A, et al.. Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students. JAMA Network Open. 2022;5(2):e2149008. doi:10.1001/jamanetworkopen.2021.49008.

5. Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. A Review of Challenges and Opportunities in Machine Learning for Health. AMIA Jt Summits Transl Sci Proc. 2020;2020:191-200. Published 2020 May 30.

6. Kolluri S, Lin J, Liu R, Zhang Y, Zhang W. Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review. AAPS J. 2022;24(1):19. doi:10.1208/s12248-021-00644-3.

7. Nelson SD, Walsh CG, Olsen CA, et al. Demystifying artificial intelligence in pharmacy. Am J Health Syst Pharm. 2020;77(19):1556-1570. doi:10.1093/ajhp/zxaa218.

8. Qazi, S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 39, 120 (2022). doi:10.1007/s12032-022-01711-1

9. Rong G, Mendez A, Bou Assi E, Zhao B, Sawan M. Artificial intelligence in healthcare: Review and prediction case studies. Engineering (Beijing). 2020;6(3):291-301. doi:10.1016/j.eng.2019.08.015.

10.Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making. 2021;21(1). doi:10.1186/s12911-021-01488-9.