AI in healthcare is nothing new, in fact it has been around for years. The healthcare industry has always been at the forefront of applying machine learning and artifical intelligence models to advance research, treatments and overall efficacy of medicine. However, the past models have been focussing on narrow AI, where each model only serves a very specific problem, use case or area of application. The typical example would be deep learning that is algorithm based and aims at predicting the presence of a gene mutation in brain tumors.
From narrow to broad AI
The disruptive element in digital healthcare has been the rise of generative AI solutions. “Generative” means that the AI is able to generate new output in a non-predefined form, based on input given in a rather free structure. These new, “broad” AI models are not designed to only perform one single task, but are capable of handling a much wider range of questions and problems based on the central model, the so called Large Language Model (LLM). The attention in the healthcare sector recently shifted towards generative AI solutions which unlocked a lot of new possible use cases. The focus and impact of these solutions are also changing, as efficiency and lowering administrative burdens unlock short-term potential and free up critical resources to focus on more value-enhancing tasks.
Healthcare AI passes exam
An examplary breakthrough event in recent months has been the launch of Med-PaLM 2, a Large Language Model from Google Research, designed for the medical domain, being able to answer questions that before only doctors and other medical staff could answer. The fact, that the prior Med-PaLM version was the first LLM to surpass the pass mark on US Medical License Exam (USMLE) style questions, shows how quickly things are developing with AI. The latest model now achieves a 86.5 % accuracy on those questions.
We are therefore seeing a rapid development of AI in healthcare. There is still a long way to go before generative AI becomes part of everyday medical practice – this is more of a long-term, very broad future trend. However, AI has a whole range of interesting use cases in the healthcare sector, some of which could become standard quite soon. We see several short-term, mid-term and long-term trends: While generative AI can already be used in healthcare administration, we at OMMAX expect to see it applied in direct interaction of patients and professionals, especially in knowledge transfer, and ultimately in advancing medical research and science itself. More specifically, we see the following AI healthcare trends.
Short-term trend: AI in healthcare administration and HR
What is already happening to a certain extent is the use of AI for administrative tasks. These can be:
- Reduction of paperwork
- Reporting & billing
- Error prevention/reduction
- Appointment scheduling & staff planning
The result is a significant gain in efficiency: AI can help to free up critical staff capacity to work more with patients (instead with paperwork) and improve the quality of care. Work intensity for medical staff can be improved, and costs for clinics, nurseries and practices can be reduced.
Another field of short-term application is the acquisition of employees or patients. AI is becoming increasingly established in marketing and lead generation and helps to implement omnichannel strategies effectively and efficiently.
Mid-term trend: AI-augmented patients & professionals
In the medium term, it is becoming apparent that AI will change the interaction between patients and doctors. In the following respects, for example:
- Personalized treatment plans for individual patients
- Best practice treatments, studies and research guidance
- Data-driven patient anamnesis
- Smart search engine for patient education
This means, that it will be easier for doctors to access state-of-the-art knowledge, in order to improve diagnosis and treatments. From a patient’s perspective, AI will foster a better understanding of medical knowledge and improve the communication with the doctor. All this might happen primarily with virtual healthcare assistants: chatbots that assist patients by answering their health questions and aid clinicians by providing advice on treatments, diagnoses and medications. Furthermore, medicine will most likely become more personalized – which is believed to result in better patient outcomes and more efficient resource utilization.
This trend is also linked to other interesting developments in the healthcare sector such as IoT-powered virtual hospitals and Telemedicine 2.0: Wearable IoT devices enable remote patient monitoring and communication with healthcare professionals, leading to a comprehensive approach to remote patient care. Consequently, preventive healthcare is also facilitated: Technology, like AI and wearables, will play a crucial role in early warning and rapid intervention.
Long-term trend: AI for smarter science
We expect a longer-term trend towards knowledge sharing and processing in medical science. This includes, for example:
- Interpretation of clinical data
- Faster identification of patients for trial
- Distribution of discoveries to other languages and complexity levels
- Faster publishing
This will allow a simplified access and use of data, reduce costs and, most importantly, speed up medical research and development, leading to an overall improved healthcare state for society.
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