Transformation of Medical Care using Artificial Intelligence-Based Diagnostics

Background

Artificial intelligence (AI) has revolutionized different aspects of medical care including delivery, management, and diagnosis. Therefore, AI and diversified diagnostic tools were the focus of several research projects and publications.

During the last 10 years, AI and diagnostics research has attracted significant attention, particularly between 2014-2018, when the number of publications in this field substantially surged (Figure 1).

Multiple research groups have investigated the impact of AI on the diagnostics of a wide range of diseases including cancer, asthma, pediatric and neurological diseases. A decent number of AI applications have been used for identifying different stages and classifications, genetic and molecular characterization, and designing targeted management plans for several diseases.

In addition, AI has reshaped the architecture of medical imaging such as endoscopy, and computerized tomography, which represent key components of the diagnostic process. Moreover, AI has contributed to the advancement of supporting clinical procedures including monitoring of anesthesia depth, prediction, risk assessment, and post-anesthesia care.

Designs and protocols

Differential protocols have been used to investigate the impact of AI in transforming diagnostics and to design studies to leverage AI tools to conduct more focused research.

AI has been used extensively in interpretation of the outcomes of medical imaging studies. In addition, studies on genetic variation and patterns have been conducted using different AI techniques (Figure 2).

Further studies have been performed to assess the impact of neurological disorders on vital functions such as hearing and speech. AI-assisted analysis of voices and speech has enhanced the outcomes of the studies that were designed to understand the pathological backgrounds of these disorders.

Moreover, studies have been designed to highlight the role of diagnostic chatbots, remote patient monitoring and clinical decision support system in transforming the current medical care system (Figure 2).

Selected free full-text articles

• Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020 Feb;132(2):379-394. doi: 10.1097/ALN.0000000000002960. PMID: 31939856; PMCID: PMC7643051. https://pubmed.ncbi.nlm.nih.gov/31939856/
  
• Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090. PMID: 33811123; PMCID: PMC8034385. https://pubmed.ncbi.nlm.nih.gov/33811123/
  
• Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T. Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med. 2022 Jun 30;60(12):1974-1983. doi: 10.1515/cclm-2022-0291. PMID: 35771735. https://pubmed.ncbi.nlm.nih.gov/35771735/
  
• Niu PH, Zhao LL, Wu HL, Zhao DB, Chen YT. Artificial intelligence in gastric cancer: Application and future perspectives. World J Gastroenterol. 2020 Sep 28;26(36):5408-5419. doi: 10.3748/wjg.v26.i36.5408. PMID: 33024393; PMCID: PMC7520602. https://pubmed.ncbi.nlm.nih.gov/33024393/
  
• Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, Alam MK. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review. Biomed Res Int. 2021 Jun 22;2021:9751564. doi: 10.1155/2021/9751564. PMID: 34258283; PMCID: PMC8245240. https://pubmed.ncbi.nlm.nih.gov/34258283/