AI in healthcare:
Augmenting Human Expertise with Technology
The true power of AI is yet to be seen. With the right framework and protocols, there is enough evidence to advocate for its ability to unburden the healthcare system and empower practitioners and patients. AI, particularly large language models, require multi-disciplinary collaboration and investment to usher in the future of healthcare.
Grasping the Essence of Digital Transformation
AI and large language models have entered centre stage, causing great excitement and much trepidation across various industries. Regardless of one’s response, whether intrigue or fear, these tools are here to stay. AI and LLM have notably impacted medicine, yielding positive and unintended consequences. This article seeks to unpack the opportunities for AI and LLM in medicine and the need for thoughtful innovation to alleviate potential concerns. Inevitably healthcare practitioners will become increasingly reliant on this technology. Therefore, a collaborative effort from academia, data specialists, tech innovators, businesses and practitioners is required to support the transition.
Healthcare without AI and LLM
Quality healthcare is foundational to a functioning humanity. A paradigm shift is required to keep up with the ever-evolving demands and discoveries within this field. A few of the challenges burdening the healthcare system today include diagnostic and therapeutic errors due to human factors, lack of resources, poor access to quality care in low-income regions, an abundance of data and research which at times yields conflicting outcomes, administrative inefficiencies, shortage of personnel, the multiplication of the burden of disease and diminished health literacy amongst other things. AI may not be the silver bullet to solve the challenges mentioned above. However, there is a potential for innovative technologies to alleviate pressures and empower medical practitioners to advance the profession.
The potential benefits of AI and LLM for practitioners and patients
AI and, in particular, LLM technologies can execute the following natural language processes task: language generation, machine translation and question-answering, amongst other skills. These could alleviate multiple burdens within the healthcare system, benefiting practitioners and patients. AI has the potential to improve diagnostic accuracy, predict disease progression and support clinical decision-making. For instance, algorithms are already outperforming radiologists at spotting malignant tumours and guiding researchers in constructing cohorts for costly clinical trials.
With an ability to process large portions of data, AI can rapidly develop specialised knowledge for different medical disciplines and fine-tune domain-specific medical literature to be adapted to various languages and contexts. This will facilitate improved global access to medical knowledge and expertise.
Additionally, a persistent challenge within the healthcare system is inequality, characterised by the inability of patients to keep up with the high cost of quality care and the limited dissemination of knowledge, expertise and care to low-income communities. AI could bridge the healthcare funding gap by reducing overall healthcare spending and boosting efficiency through big data models, telemedicine and predictive medication. Therefore, there is a need to accelerate the investment and research into AI in healthcare to bring about these advancements sooner.
Prominent Risks at Play
However, AI is still experimental and therefore has its complications. For instance, platforms such as ChatGPT-4 and Med-Palm from Google Research are constantly being updated and improved in response to environmental factors and feedback. Therefore, it is inevitable for there to be unintended consequences at this stage of AI usage. Risks include data vulnerability; privacy is required to ensure fair and equitable healthcare. Secondly, an over-reliance on AI technology could reduce human input in decision-making processes. Therefore, there should be a focus on augmenting human expertise rather than replacing it. Lastly, there may be high upfront cost implications which could be a barrier to widespread adoption. The above is not an exhaustive list, but it is evident that the risks associated with AI in healthcare must be mitigated for a significant positive impact.
Mitigating Factors Needed to Prepare for a Future of Medical AI
As risks and challenges brought about by AI surface, mitigating factors can be implemented.
Collaboration among medical professionals, data scientists, ethicists, and policymakers is essential to foster innovation and accelerate the translation of research findings into practice. Additionally, it is imperative to make room for diverse perspectives, such as patients, caregivers, and representatives from different cultural backgrounds, in the development of AI. This will ensure inclusivity and greater access to technological solutions.
For AI to produce accurate data, there needs to be a prioritisation of recent and highly cited articles within medical academia. Additionally, practitioners need continuous professional development programs to ensure they remain competent in using the latest advancements in AI technologies. Lastly, as AI is developed, transparency is vital for maintaining public trust and fostering ethical use.
A Positive Impact
The advancement of AI technologies and the potential possibilities could provide tremendous acceleration in the advancement of providing an inclusive healthcare system globally. To ensure ethical, accurate and inclusive application, continuous research and development is required, together with multidisciplinary collaboration and precautionary measures. If executed well, AI in healthcare could be an avenue for high financial returns for investors with a positive impact. It is imperative to stay current with changes within this high-potential industry.