Artificial Intelligence (AI) has become increasingly integral to medical practices, with applications ranging from administrative tasks to diagnostics, patient communication, and logistics optimization. Numerous studies have demonstrated the effectiveness of AI algorithms in daily clinical practice. For instance, AI-assisted mammography and treatment planning have shown promising results in diverse settings, including both in Sweden and the U.S.
In the field of radiotherapy for cancers such as lung, prostate, and colorectal, AI technologies are being harnessed to accelerate treatment planning. This not only reduces the workload for healthcare professionals but also improves patient outcomes – a true win-win scenario we are looking for when designing these studies.
Artificial intelligence has firmly established its presence in the medical field. The primary consideration is not whether AI will be integrated into standard care, but rather how it will be implemented. Unless one has been metaphorically sleeping through the past year, much like a 21st-century Sleeping Beauty, the rise and relevance of AI in medicine is unmistakable.
Are patients to pay the cost of AI in their care?
As AI finds its way into everyday clinical settings, a critical question emerges: who will pay for the deployment (and use and maintenance) of such systems? And this is not a sci-fi question, but something we face today.
This article discussed how the author was asked if she wanted to pay $40 extra for additional AI analysis in mammography. In her case a Manhattan radiology clinic offered an AI analysis of their mammogram for an additional $40, not covered by insurance. This scenario was echoed at a clinic in suburban Baltimore, where patients were similarly offered AI-assisted mammography for a $40 fee. These instances mark the initial real-world applications of AI in patient care but also introduce new factors to the healthcare equation.
To make things more complicated, we can’t look for a single, universal solution here. Healthcare systems all over the world are extremely diverse, and there will be no “one size fits all” answer that is equally applicable to the private insurance-based model in the USA to the tax-funded public healthcare in Scandinavian countries.
Will AI be cheaper or more expensive than doctors?
The question of whether AI will be more cost-effective than traditional medical practices is complex, and the earlier cited examples, where patients were charged extra for AI-assisted mammography, do not necessarily represent a universal pricing model for AI in healthcare. In the future, the cost comparison might not be straightforward and could involve weighing the price of a doctor’s time against the operational costs of an AI algorithm.
Consider the scenario of laboratory tests. If AI can provide sufficient analysis at a cost of X, and you need to pay 2X for a doctor to review, relying on the algorithm’s assessment becomes a cost-effective option. But of course, it may happen the other way around, it is too early to know that.
Conversely, while AI might offer an additional layer of analysis for imaging tests like MRIs, this could potentially come at a higher cost. The financial implications of AI in healthcare are still evolving, and it’s unclear how these will reshape overall costs.
In countries with private insurance-based healthcare systems, the key factor is insurance coverage: will plans adapt to cover AI-enhanced services, and how will this affect premiums and out-of-pocket expenses? In countries with socialised medicine, the question is whether there are sufficient funds to deploy AI technologies in the first place.
New divides will be apparent on multiple levels
The integration of AI in healthcare, at least in the short term, threatens to create new disparities in access on multiple levels. A clear example is the AI-assisted mammography scenario: those who can afford to pay more receive additional services. Studies show better detection rates with AI, and while the routine value of such technology in clinical practice is still under evaluation, we can take it for granted that access will not be universal.
You don’t even need to have a private insurance-based system to face this issue. Let’s consider moderately wealthy countries with socialised medicine and healthcare systems – like the B-tier of the developed world. While such nations often boast relatively advanced healthcare systems, their lower GDP results in significantly less funding compared to the wealthiest countries. In such environments, we’ll more likely see AI-assisted solutions in private care, but not (or not much) in the public system.
This situation also creates disparities: access is often tied to one’s ability to pay. Yet, in these countries, these inequalities are obscured behind the facade of “free healthcare.” This covert inequality is likely to affect many moderately wealthy nations as they struggle to incorporate AI into their public healthcare systems.
Furthermore, we are likely to witness divides between countries and even continents. Wealthier nations with state-funded healthcare systems might introduce AI more rapidly compared to less affluent countries. For instance, Hungary might struggle to keep pace with countries like Sweden or Germany in universally implementing these technologies.
And of course, there will be a much more pronounced difference between third-world countries and the wealthiest societies.
Languages also present a barrier, as countries speaking languages with a larger global presence, such as English, Chinese, and Spanish will have more readily developed and implemented systems compared to odd, small languages spoken by a few million.
Between wealthy and struggling providers
The financial capability to invest in such technologies significantly impacts their availability and implementation. Thus, many systems might become available for some and unavailable to others, like an AI product reducing physician workload and improving efficiency may be more easily adopted by well-funded private clinics than by struggling state healthcare systems.
Leapfrogging in the third world?
However, there’s an upside: AI could enable ‘leapfrogging’ in less developed countries. These nations might bypass the need for certain intermediate infrastructures that are costly to maintain in developed countries. This could be a stepping stone in their development, potentially turning a previous disadvantage into an opportunity. For example, AI-driven mobile health applications in remote areas could provide diagnostic support where access to healthcare professionals is limited, effectively leapfrogging the need for extensive healthcare infrastructure.
AI also presents a solution to the shortage of healthcare professionals, a challenge particularly acute in poorer regions. The migration of medical staff to wealthier areas exacerbates this problem, making access to quality healthcare even more challenging. AI could bridge this gap, offering a level of diagnostic and treatment planning support in regions where human resources are scarce.
Saying all this, it’s clear that the cost of implementing AI in medicine is not a simple issue with straightforward solutions. Healthcare systems worldwide are in a phase of transition, grappling with how best to integrate AI technologies into their frameworks. Regulators, too, face the challenge of establishing guidelines that balance innovation with accessibility and fairness. As patients, we must understand this evolving situation and our options within it.
Staying informed and engaged is crucial, as these technological advancements hold the potential to significantly alter our healthcare experience.
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