Generative AI is Unlocking the Healthcare That We Need
Dan Lubetsky
Customer Engagement Executive | Strategy & Solutions Leader | AI Enthusiast
The image above is not the only possible future of healthcare. While wearables and connected devices are popular and can help with diagnostics, emergency alerts, and ways to track and manage improvements in health, it does not stop there. Artificial Intelligence (AI) is already used across many departments and systems in the healthcare space and Generative AI will push the boundaries of patient care, shake up the doctor's toolkit, and put more power than ever into the hands of individuals. But we need to tread carefully; there are hurdles like data privacy, AI bias, and legal headaches to clear. AI's all set to transform healthcare, but we have to strike the right balance between human touch and machine intelligence.
AI and the Doctor's New Bag of Tricks
Generative AI is turning healthcare on its head. It's making care more personal and handing doctors new and improved tools. By taking a deep dive into tons of data, from electronic health records to genetic tests, AI can spot patterns and predict outcomes, resulting in unique, customized care plans. On top of that, AI is giving the doctor's toolkit a much-needed upgrade. It's all about quicker and more precise results. For instance, it can sharpen MRI images, even those with lower resolutions, enabling physicians to interpret them more clearly and quickly. AI algorithms can also analyze these images and provide information about conditions like cancer, bypassing the need for human interpretation. This not only expedites diagnosis but also helps deliver timely and precise treatments.
Take JILL.ai from MediKarma, for instance. This AI-powered health assistant is changing the way we think about preventative and value-based care. JILL.ai is a domain-specific model trained on industry-leading literature from trusted sources like the World Health Organization. The benefit? Instant health information, answers to health queries, and personalized recommendations. It's about giving people the power to make better decisions and take charge of their health.
Additionally, Tempus One, an AI-enabled clinical assistant developed by Tempus provides doctors with quick access to a patient's comprehensive clinical and molecular profile in real-time, enabling them to offer more precise and personalized care. Tempus One demonstrates how AI can augment a physician's capabilities, improve their efficiency, and ultimately deliver higher-quality individualized care.
By integrating AI into their practice, doctors can manage the wealth of information associated with each patient more effectively and deliver more personalized care. But remember, the goal isn't to replace doctors with machines. AI is about enhancing what humans can do, not doing away with them.
Next Up: Power to the Patients
The next big thing? Putting patients in the driver's seat. Generative AI is set to play a huge part in this shift. But its potential isn't limited to analyzing complex health data and suggesting personalized treatments. AI can also motivate individuals to change their behaviors.
Imagine someone with a chronic condition, like diabetes. Generative AI can look at their health data, suggest a tailor-made treatment plan, and then step in to help them stick to it. This could be anything from reminding them to take medication, tracking their progress, or offering words of encouragement. This hands-on approach could lead to patients sticking to their treatment plans and improving their overall health.
But there's a tricky question we need to answer: who's in control? While patients should be calling the shots, doctors play a key role in making sense of AI's suggestions. There are also potential issues related to insurance, data privacy, and equal healthcare access that we need to sort out. Unsupervised AI could pose risks if misinterpreted or misapplied. It's essential to find a balance where AI augments, not replaces, human judgment and patient autonomy.
When considering the insurance implications, it's a double-edged sword. On the positive side, AI-driven treatment plans could improve health outcomes and potentially lower healthcare costs. However, there's also a risk that insurers could increase rates based on AI's predictive insights into a patient's future health risks. Also, privacy concerns could arise if insurers had access to the detailed health data used by AI. These concerns highlight the need for clear regulations to protect patient rights and prevent health inequities.
As we navigate this, it's imperative to address potential challenges and risks, including data privacy, AI bias, and emerging malpractice concerns.
Opportunities and Speed Bumps: Navigating the AI Landscape
Generative AI opens up a world of possibilities in healthcare, but it's not all smooth sailing.
Data Privacy and Security: With AI comes the responsibility of safeguarding sensitive healthcare data. Compliance with regulations such as HIPAA is a bare minimum, and additional measures are necessary to prevent misuse of this data.
AI Bias and Inequality: Biases in AI can arise if the training data are not representative of the broader population, a problem that’s historically prominent in healthcare. For instance, many clinical trials have been criticized for underrepresenting certain demographic groups, such as women, elderly individuals, and racial and ethnic minorities. If an AI system is trained on such data, it might be less effective or even erroneous when used for these underrepresented groups. Similarly, socioeconomic bias may exist if AI is trained predominantly on data from higher-income individuals who have had more access to healthcare services. This could lead to AI systems that are less accurate or beneficial for patients from lower socioeconomic backgrounds. As such, using diverse and representative datasets for AI training is critical to prevent the perpetuation of these biases and ensure equitable care for all.
Reliance on Technology: An over-reliance on AI could potentially diminish physicians' diagnostic skills over time. Striking a balance between using AI and maintaining human expertise and judgment is essential.
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Malpractice Concerns: Perhaps the most complex challenge is the potential for malpractice and the question of liability if an AI system errs in diagnosis or treatment recommendation. The intersection of patient experience, physician interaction, and treatment outcomes is where this concern becomes most relevant. Patients need to be aware of and consent to the use of AI in their care, but understanding its role can be challenging, potentially leading to a knowledge gap and perceptions of malpractice. Physicians, too, face a dilemma. While AI aids decision-making, they must discern when to follow AI advice and rely on their judgment. The liability issue in the case of AI errors, particularly with systems that learn and adapt over time, adds to the complexity.
The future of healthcare could be bright with generative AI. It could make healthcare more efficient, accurate, and patient-friendly. But, it's a road we need to tread carefully. We need to manage the potential challenges around data privacy, AI bias, over-reliance on technology, and malpractice risks. Striking a balance between the powers of AI and human judgment is a must. And, clear regulations are essential to ensure that AI serves individuals without compromising their safety or trust. If we get this right, generative AI could make healthcare a much more empowering experience, helping individuals play an active role in their health journey with the help of AI. Let's buckle up and prepare for this exciting new chapter in healthcare.
Acknowledgment:
Thanks to Jimmy Lepore Hagan for your support on this edition. You had some great insights!
References
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Director of Community Management
1 年I'm a big believer of seeking out that ideal balance between AI and Human powered insights and decision-making! My ideal scenario is that AI serves as an assistant that we can course-correct and choose to accept or reject (without hurting its feelings). There's plenty of precedent for this, remember clippy?