- Surgical training and education
- Preoperative assessment and diagnosis and clinical decision support
- Intraoperative surgical performance and quality improvement
- Smart OR’s and environment
- Postoperative care and complication risk assessment and predication
- Workflow improvement, like OR start time and turnover efficiencies
- Surgical robotics
- VR and AR and 3-D printing
- Telerobotic and teleproctored surgery
- Human subject trial recruitment and execution
Many of these applications are in the research or demonstration phase and will take a while to become the standard of care or achieve widespread dissemination and implementation.
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 using scarce resources 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 is how doctors and patients can win the 4th industrial revolution.
What is the secret sauce of successful innovators like AIntrepreneurs? They strive to innovate in ways that would have a major impact on markets and society, e.g changing sick care to health care or making the sick care workforce more efficient and effective, and they revamped how their organizations pursued innovation and brought their capabilities together in a single “architecture.” That will mean medtech transforming to techmed will require changing how to collaborate with doctors and patients.
The scenario may seem farfetched or at least far premature given the state of the technology in 2020. However, it’s not too soon to begin thinking such things through, suggest the authors of a new whitepaper on the ethics of AI in, specifically, surgery.
Why is the gap between companies’ AI ambition and their actual adoption so large? The answer is not primarily technical. It is organizational and cultural. A massive skills and language gap has emerged between key organizational decision makers and their “AI teams.” It is a barrier that promises to stall, delay, or sink algorithmic innovations. And it is growing, not shrinking. The problem is that healthcare professionals are from Venus and technologists are from Mars. They have different mindsets and how they communicate.
These are ten major takeaways from a recent conference in AI in Surgery
- We need de rigueur evaluation systems to assess AI tools in order to assure patient safety and clinical efficacy.
- Surgeons as well as ICU and anesthesia clinicians need to deploy AI for preoperative risk assessment and postoperative care protocols while embracing complexity that inevitably exists with biomedicine.
- Advances in surgery utilizing AI include not only robotic surgery but also image-guided surgery (such as MRI/ultrasound fusion guided biopsy techniques or surgical procedures).
- Outcome prediction for various surgical procedures can be rendered much more accurately with use of AI methodologies such as machine and deep learning.
- The disease burden of septic shock needs to be ameliorated with a cohesive effort amongst caretakers to utilize existing AI tools such as deep reinforcement learning.
- The future of patient sensor data and accompanying platforms as well as the medical internet of things will need to be coupled to AI for impact.
- AI in surgery and ICU/anesthesia warrants a team approach that includes members from IT, data science, database managers, clinical domains, and others in order to execute AI agendas.
- The future of medical education and clinical training will need to include basics of data science and AI especially within a clinical framework.
- A future dividend not often mentioned is the clinicians appreciation and understanding of healthcare data with its myriad of nuances (medicine is no longer a biological science supported by data but rather a data science based on biological beings).
- The AI strategy of swarm intelligence will provide clinicians and patients with the collective wisdom of the group (best solutions are usually from a group rather than a few experts)
Those products and services that add value, particularly those that drive down costs and do not interfere with workflow, have a higher chance of success. Those that don’t will be relegated to the shiny new object pile and red bagged for disposal.