Intelligent AI technologies find the right solution for your pharmaceutical or medical business


By Kathleen Warner, Ph.D., RCM Technologies

2022 brings exciting new smart technical solutions that address patient and product safety in real time. As we apply these technological solutions, sometimes referred to as the era of Life Sciences (LS) industrialization, we need to understand the power of these solutions and the new ways to approach patient and product safety. Questions posed and discussed relate to standards for codes/algorithms and government regulations, as well as ethical and social responsibility to ensure that these technologies are used appropriately and correctly. To avoid exposure to public criticism, it is important to note that AI systems can be programmed to make mistakes, be biased, and prone to computer predators (unauthorized users who break into computer systems with the intention to alter and/or destroy information). Note that these technologies are not to be feared and avoided; rather, they should be adopted and included in the Disruptive Technologies and Internet of Things (IoT) strategy and framework.

This article takes a closer look at smart technologies and what we can expect in 2022 and beyond. It explains how AI and smart technologies are being used in the LS (i.e. biotechnology, medical device and pharmaceutical industry) and healthcare sectors, identifies some of the emerging concerns and challenges related to disruptive technologies and risk management, and discusses a technology and build timeline. .

As we have experienced in the past, standards bodies can be both positive and negative; however, they are necessary. Some countries have developed and published standards for Data protection (UK), and others address ethical principles and standards of conduct in their code of ethics for AI (Russia). AI and ML standards ensure that these technologies will be used in a positive and unbiased way to improve patient and product safety and advance computer machine learning. However, an argument is brewing around the need for global standards for AI and ML. We can learn from the past; the World Wide Web (W3C) global consortium helped pave the way for the Internet and addressed some of the same issues around ethical and social responsibility in addition to principles and standards.

Technological risk categories

In a recent risk study among senior executives, 72% indicated that new, complex and interconnected risks are emerging faster than ever. Risk categories included:

  • Disruptive Technology Risk: 58% of respondents indicated that disruptive technology risk has a greater impact today than in the previous two years.
  • Data Security and Breaches: 55% of respondents indicated that data breaches are bigger and pose a more complex threat to businesses.
  • Operational risks: 52% of respondents said operational risk, a traditional but pervasive threat, was a concern. Other examples of operational risk (not part of the risk study) include human and technical error, intentional fraud and uncontrollable factors (i.e. heredity, age, etc.) and flow deficiencies of work.

Some of the biggest risks to innovation and disruptive technology in 2020 were:

  • Compliance and Legal Violations —This covers privacy and consumer data protection risks in the cloud. Businesses should keep up to date with data protection laws in the UK (GDPR) and the US (HIPAA). Employees, partners, consultants and customers all want to know that their data is protected.
  • Data Breaches – As noted above, data breach incidents can be mitigated by having policies, procedures, and processes that deal with incidents head on or before they happen. Refer to additional information on the cost of data breaches here.
  • User Privacy — Companies must be able to protect PII (personally identifiable information) and keep it out of the reach of hostile actors.
  • Justice and Equity – As stated earlier, ML is very important for new learning methods, but only if human biases are omitted and models and datasets are also bias-free.
  • Reputation risk – The AI ​​chatbot is an example where the bot’s interactions with other social media can cause embarrassment to the business, resulting in the removal of the chatbot and requiring the business to do damage control.
  • Spoofed chatbots — These are chatbots that appear to be a legitimate business in an app store. Once downloaded, the chatbot can access company data.
  • Ethical and legal concerns – As mentioned above, identify the need for principles and standards to ensure patient and product safety and difficult ethical questions.
  • IoT – With more and more complex devices connected to the IoT by 2022 (including smartphones, GPS and smart devices), the IoT is wide open to new attacks around the world. Billions.
  • public safety – To prevent IoT breaches and manage public infrastructure, physical security and cybersecurity are essential.

The risks associated with innovation and disruptive technology are global and not limited to life sciences or healthcare. To name a few, governments, armed forces, financial companies, etc. all face ways to manage and prepare for disruptive new technologies, data breaches and operational risks. The risks are very real, and attackers, hackers and intruders are only steps behind as they continue to gain traction using disruptive technology for all the wrong reasons. Refer to Spotlight on technology risk report for a more in-depth look at risk and risk resilience.

How AL and ML are used in LS and healthcare

AI and ML are transforming the way we explore, develop, understand, communicate and collaborate. With the emergence of disruptive innovations, scientists and statisticians in the LS (i.e. biotechnology, medical device, and pharmaceutical industry) and healthcare sectors can seek gigabytes at terabytes (i.e. measurement of binary data) of data sets associated with medical treatments and medical solutions. For example, in a recent BBC study, “AI technology was superior at detecting breast cancer in female patients and had a 1.2% lower number of false positive diagnoses. This is very promising data indeed and a trend that will likely continue with the weather.

Table 1: Use of AI and ML technologies in life sciences provides several current use cases using smart technologies in three categories: people, technology and business.

Table 1: Use of AI and ML technologies in life sciences

AI Early Adopters: Pharmaceuticals, Employees, Healthcare, and Startups

Some early adopters, including pharmaceutical companies and their collaborators, are already using AI and ML to develop AI and ML tools that can be used to advance smart science, technology and medicine: AbbVie , Amgen, Gilead Sciences, GlaxoSmithKline, Johnson & Johnson, Merck & Co., Novartis, Pfizer, Roche and Sanofi.

Another notable AI startup healthcare companies with specific goals and objectives brings to market new and innovative ways to improve the patient experience, extend human intelligence, address patient risk and ensure data integrity through to improved operational requirements.

Conclusion

This article has identified several examples of how AI and smart technologies are transforming the world of life sciences and healthcare. As mature Gen Zers, they are synonymous with automating everything, everywhere in technology. They have an innate ability to develop technology-driven solutions on their own and are comfortable with new programming languages ​​(Python), as well as AI and ML. What we’ve learned from past generations in technology to what we know now begs the question: Could the technological advancements made by Gen Alpha lead to the next computing phenomenon of all time?

Figure 1: Flashback Technology Generations provides a chronological overview of the last four generations of technology and where the fifth generation technology Alphas will lead.

Figure 1: Flashback – Technology Generations

The future of Gen Alphas will be surrounded by current and new disruptive technologies: prime examples include AI, blockchain (see Bitcoin and how banking is disrupted), 3D printing, virtual reality (VR)/augmented reality (AR), robotics, and IoT. Let’s track the progress of Gen Z and Gen Alpha, the application of smart technologies and the adoption of these technologies by LS and healthcare. By discovering and developing concrete solutions, enabled by disruptive technologies, we can treat, cure and/or prevent diseases. Technological utopia becomes an objective accessible to all.

About the Author:

Kathleen Warner, Ph.D., vice president of consulting services for RCM IT and Life Sciences, is an executive consultant with over 25 years of experience in information technology (IT) and life sciences. She has been a chief information officer, subject matter expert, and subject matter expert in regulated environments. As a management consultant, Warner has overseen hundreds of life science projects in the United States and around the world. His strengths include leadership, consulting, organizational change management, business process analytics and program/project management assignments. As a practitioner and technologist, Warner has performed future cloud assessments and provided transformation program services for IT, R&D and quality.

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