Biomedical AIntrepreneurship describes the practice of creating AI products and services to solve bioscience (drugs and devices) and clinical problems. As such, it is the pursuit of opportunity under conditions of uncertainty with the goal of creating user defined value through the design, development and deployment of biomedical innovations that use a predominantly AI backbone, platform or foundation that have a VAST business model. It is a subsegment of digital health products and services.
The use of AI in medicine is evolving rapidly. Here are some updates:
- Educational platforms, meetings, conferences and magazines
- Robust investment into development and M/A
- Coherent applications combining AI, medtech and biopharma
- Increasing concerns and attention to the ethical, societal, education, manpower development and economic impact of AI in medicine
- How AI is contributing to the 4th industrial revolution
- The intersection of AI and robotics
- The intersection of AI and blockchain
- The impact and perils of decentralized, DIY medicine
- Cybersecurity and confidentiality concerns. If you are not worried yet, read this too.
- Concerns and strategies to make transparent algorithms and mitigate AI bias and “eliminate black box bias”.
- Stories and organizations about physician AIntrepreneurs.
- Regulatory, legal and reimbursement challenges
13. Convergence of AI into medical device and biopharma Current emerging applications appear to fall into three main categories:
Management of chronic diseases – Companies are using machine learning to monitor patients using sensors and to automate the delivery of treatment using connected mobile apps (Example: Diabetes and automated insulin delivery).
Medical imaging – Companies are integrating AI-driven platforms in medical scanning devices to improve image clarity and clinical outcomes by reducing exposure to radiation (Example: GE Healthcare CT scans for liver and kidney lesions).
- AI and Internet of Things (IoT) – Companies are integrating AI and IoT to better monitor patient adherence to treatment protocols and to improve clinical outcomes (Example: Philips Healthcare solution for continuous monitoring of patients in critical condition).
As artificial intelligence projects roll out, organizations will need to rethink the definition of the “work” that people will do. The future of work will become one of the largest agenda items for policy makers, corporate executives and social economists, says Sanjay Srivastava, chief digital officer at Genpact, a professional services firm focusing on digital transformation. Here are some of the key issues.
In addition, as part the 4th industrial revolution, AIntrepreneurs are creating a sick care environment that will challenge how we educate medical trainees and computer and data scientists to work together and figure out how to overcome the barriers to AI dissemination and implementation, the IoMT challenges and the legal, economic, societal and ethical hurdles.
The implications of the convergence of AI with other technologies in multiple industries are vast. However,the value remains to be demonstrated. What is clear is that AI is the latest shiny new object on the block and will continue to grow, and, hopefully, realize its potential to reduce costs, improve outcomes and efficiencies and the doctor and patient experience.Innovation starts with mindset and there is nothing artificial about that.
Image Credit: Pixabay
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