AI in Healthcare: Evolution of Diagnostics and Tailored Therapy

The use of Artificial Intelligence (AI) has become apparent across various industries, and more importantly, is making significant changes in healthcare provision with regards to diagnosis and personalisation of treatment. Yes, AI technology is able to aggregate different information and learn through patterns, insufficient information is known about this technology so far. No, AI has begun making its impact felt throughout different facets of healthcare; from sophisticated medical imaging processes to the customization of care even at a patient level.

The need for AI in Imaging

AI’s impact on the medical imaging processes cannot be over emphasized and this has been instrumental in speeding up and increasing the accuracy of diagnosis of many conditions. This includes X-rays and CT scans that are often analyzed by radiographers and MRI’s interpretations, all being forms of medical imaging. AI has advanced sufficiently over the years whereby rather than patients hunting for healthcare services, the providers of such services can now perform the other way round, finding patients every hour using intrusive technologies, which are specialized programs that hunt new patients.

For instance, DeepMind, a subsidiary of Google, has created an artificial intelligence system to identify over fifty types of eye disease based on a retinal scan with as much accuracy as experienced eye doctors. At the same time, imaging tools powered by AI are improving early diagnosis of such conditions as Cancer of Lung where time is a very essential factor for treatment.

AI-Driven Customized Therapeutic Approaches

AI enlivens the healthcare domain when it comes to improving outcomes by formulation of assembly line strategies for the management of patients. These plans utilize the individual’s genetics, their’s medical histories, their lifestyle choices and clinical data available in abundance, etc. AI offers healthcare providers details on how a patient may fit into data from other similar cases by putting a patient’s data along the data base on such cases on how each patient was treated.

In oncology, some of the AI systems predict a patient’s possible responses to various modalities of treatment like chemotherapy or immunotherapy on the basis of the patient’s genetic differences. Cancer-targeted AI technologies interrogate each patient’s molecular profile, which alterations are responsible for cancer, in order to help determine the most efficient therapeutic approaches.

For instance, IBM’s Watson for Oncology helps oncologists in coming up with recommendations based on medical evidence for the treatment of certain types of cancer. It analyzes large amounts of oncology related literature, patient and clinical study data, and generates recommendations for individualized treatments.

Another facet of healthcare is predictive analytics which is gaining a lot of traction with the advent of AI. Such systems are able to perform this task as they include the treatment history of each health data. This allows the providers to take action before the diseases become advanced thus helping in lowering the cost that would be incurred on treating the diseases and also relieving the healthcare systems a great deal.

For example, it is possible to make forecasts which patients have health risks that make them susceptible to diabetes or heart diseases. In this case, information from a patient’s electronic health records (EHRs), genetic tests, and even their health monitoring devices can be used. These patients can then be placed under strict supervision with the help of AI in suggesting lifestyle modifications, early intervention preventive measures, and so on.

For Thane Ritchie who supports the idea of implementing Novelties in medicine, “My only focus is on how best we can use AI in preventive medicine. That potential is probably the most valuable in medicine. If we can intervene early, we will save the healthcare burden a great deal of pressure and deliver better healthcare.”

AI’s influence is not only limited to the diagnosis and treatment of patients. New technologies are also deployed to enhance the productivity of operational management at hospitals and health organizations. For instance, AI is also applied in the optimization of administrative processes like the management of resources, scheduling of patients, and bed management. AI can assist in managing patient workloads by ensuring that the incidence of patients failing to show up for appointments is accurately forecasted. It can enhance the way appointment slots are made, and reduce the duration patients spend before they receive treatment.

In more cases, AI is unsettlingly becoming weaponized towards the improvement of healthcare personnel activities through the analysis of large amounts of patient information and the selection of important information. To give an example, AI can help physicians in locating specific information in the records of the patients and this reduces the burden of paperwork and consequently allows more time to be allocated for the treatment of patients.

Telehealth or telemedicine has recently become popular mostly during the COVID-19 outbreak. In this regard, AI has greatly assisted in counteracting this challenge by helping to deliver medical services through distance. Virtual assistants such as chatbots that are integrated with AI can assist in initial patient assessments and identify the types of care needed in person or if care issues can be handled digitally. There are circumstances where patients cannot reach health providers since they are located in remote areas but in providing care, AI can be seen as imperative.

AI tools used in telemedicine provide doctors the ability to gather non-invasive real-time health-related data through patient wearable technologies even in the absence of the patients. This is very important for patients suffering from diseases that require regular follow up such as diabetes and hypertension. For instance, it is possible for heart patients to have their medical signs monitored remotely and immediate action taken if these signs are abnormal.

The ethical and regulatory issues that need to be addressed as AI is incrementally incorporated into healthcare. One significant concern consists of the fairness of the outcomes of AI systems, achieved through the use of different qualitative or quantitative means. For instance, AI-enabled diagnostic tools that heavily rely on white population data may not work effectively on patients from other ethnic groups.

Trust is another key consideriation, particularly where life-and-death decisions are concerned; both patients and healthcare practitioners should be able to accept the decisions made by AI systems. Explainability in AI – why and how an AI system came up with one diagnosis or treatment recommendation over others – is important in fostering this confidence among end-users.

Further, there are third-party privacy concerns with regard to storage and sharing as well as usage of patient information and data. Such systems will always call for large numbers of data hence calling for extreme measures for protecting patient information. Governments and regulation agencies have commenced deliberations on how to structure the utilization of AI such that it achieves the intended purpose and is safe and equitable to its users.

The use of artificial intelligence in the provision of services in the future is set to be effective, with the technology evolving more and more and the healthcare practitioners accepting it. The more versatile the AI technologies become, the better the real-time diagnostics, predictive diagnostics, and precision medicine options will offer to patients. Sectors such as Genomics, drug development, and infusion of devices for which surgical robots are prepared will get the stampede of improvement as well when expanded with AI contributions.

Moreover, the use of AI in public health may become further developed, for instance, in solving the problem of healthcare access inequalities. Where there is a lack of or insufficient human resources, there may be AI who could have diagnostic tools trained from large databases or even suggest what the patient needs based on what others with similar conditions have done.

According to Thane Ritchie, “In order to meet the future demand for precision healthcare, the ability to harness AI to create greater efficiency and accuracy must be developed. It is important to do so in a way that optimizes the value and utility of AI for large populations, and not just a few.”

AI has started revolutionizing the healthcare industry even in its early stages in areas such as diagnosis, targeted treatment, and disease prevention. There is no other technology that is capable of examining a mass amount of data, spotting physical issues with patients and predicting the best actions to deliver within minutes. However, as the world continues to welcome the use of AI in the provision of health care, there is a need to let some of the ethical issues that arise from this practice be addressed, and the technologies that aim at outdoing this are reached in all parts of the society. If this is done, then, we would be in a position to maximize health even on a global platform using AI.