Quoting from HubSpot (Generative AI-v4.pdf (hubspot.com), “AI can do a lot, but here’s what it cannot do (at least right now):
Original research: AI can’t conduct original research or conduct analysis. In general, AI written content is pretty surface level, so you’ll need to bring the substance and research to deepen your pieces.
Provide lived experience: AI mimics humans, but it can’t touch lived experience. So put your own lived experience and perspectives in your pieces.
Ensure quality: AI simply lays logical words down on a page. Taste and quality are not accounted for in its generations. This is why humans are essential to the AI equation.”
Can technology actually kill off human beings? “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war”. This wasn’t a bunch of conspiracy theorists. It was signed by many of the world’s technology leaders, scientists and engineers, including those at Google, Microsoft and Open AI (who developed Chat GPT).
What about our jobs? A recent report by Goldman Sachs suggests that AI tools threaten over 300 million full-time jobs in our economies.
To counter the negative effects of Generative AI, a recent article in The Thaiger suggests a three part strategy.
- Enact a human rights due diligence framework, as outlined in the UN Guiding Principles on Business and Human Rights.
- Have academics, civil society actors, and community organisations, engage with traditionally marginalised communities to influence product design and policy development processes and to better understand the potential impact of these new tools.
- In the absence of regulation, engage the human rights community to prevent and mitigate the potentially dangerous effects of Generative AI.
Caregivers complain AI models are unreliable and of limited value. Tools designed to warn of impending illnesses are inconsistent and sometimes difficult to interpret. Even evaluating them for accuracy, and susceptibility to bias, is still an unsettled science.
Clinician’s trust: While many models perform just as well as doctors in discerning disease, they can’t fully explain their logic in ways that clinicians can understand. One set of explainability methods, called saliency methods, when used in radiology, can offer a window into how these models make their predictions by highlighting the areas of a medical image that most affected an algorithm’s prediction. The result takes the form of a heatmap overlaid onto the image, as though the algorithm has drawn a border around a concentrated area of disease such as a lung nodule on a chest X-ray.
Even if you as a healthcare professional are not using AI, you can be certain that many of your patients are. They may come for appointments armed with not only Internet information, but also ChatGPT information. Another challenge here is at what reading level is ChatGPT providing information? For someone who has a low reading level, or poor English skills, they may not be understanding or interpreting the information correctly.
What is certain about AI, is that it’s here to stay.
For more articles in the series on Artificial Intelligence, go to:
- An AI (Artificial Intelligence) Primer
- AI’s Capabilities
- AI Achievements
- ChatGPT (or any AI bot) and Your Medical office
- Teaching AI in Medical School
- Patient Trust in AI Chatbots & ChatGPT
- Competitors to ChatGPT
- AI Policies and Regulatory Challenges
- Ai Bias
- AI’s Limitations, Concerns and Threats
- AI and Accountability
- The Dangers of AI
- The Future of Generative AI
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